Methods and devices for determining a position of a wearable sensor patch

Abstract

The present invention relates to a computer-implemented method for determining a position of a wearable sensor patch (182) in a horizontal direction, wherein the wearable sensor patch (182) is positioned on a user's chest, wherein the wearable sensor patch (182) comprises at least one acceleration sensor (182) configured for generating acceleration sensor data, the method comprising: ­ receiving the acceleration sensor data, wherein the acceleration sensor data is related to a walking movement of the user (196), ­ wherein the acceleration sensor data comprises data related to the user walking in a forward or a backward direction during at least one left turn of the user and at least one right turn of the user, ­ determining at least one left turn of the user (196) and at least one right turn of the user (196) while the user (196) is moving in a forward or a backward direction by evaluating the acceleration sensor data and, thereby, generating turn data, and ­ determining a horizontal position of the wearable sensor patch (182) on the user's chest by evaluating the turn data, preferably wherein the horizontal position is determined relative to a horizontal center of the user's chest, ­ wherein comparing the acceleration sensor data related to the at least one left turn and the acceleration sensor data related to the at least one right turn to each other is performed in order to determine an asymmetry in the turn data that is introduced due to a positioning of the wearable sensor patch in a horizontal direction that does not coincide with the horizontal center of the user's chest.

Description

Methods and devices for determining a position of a wearable sensor patchTechnical FieldThe invention relates to a computer-implemented method for determining a position of a wearable sensor patch in a horizontal direction, an evaluation unit for determining a position of a wearable sensor patch in a horizontal direction, a remote device, a computer program, a computer-readable storage medium and a medical system.The devices and method according to the present invention may mainly be used for longterm monitoring of body functions, such as for long-term monitoring of a mechanical characteristic of a heart of a user. The invention may both be applied in the field of home care as well as in the field of professional care, such as in hospitals. Other applications are feasible.Background artThe present invention relates to the use of acceleration sensors, preferably in order to determine gyrocardiograms. The acceleration sensor may be comprised by a wearable sensor patch. In this context, the acceleration sensors are used in order to detect motions of the chest of the user that can be associated with at least one mechanical characteristic of a heart of a user. Further information may be derived from l.Siecinski, S., Kostka, P. S. & Tkacz, E. J. Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications, Sensors 20, 6675 (2020). For high-performance detection and classification for micro-motions but also macro-motions, the orientation and / or position of the acceleration sensor, specifically in a horizontal direction is crucial.Currently, two standard methods exist to ensure a correct orientation and / or position of the acceleration sensor. In a first method, the user may orientate and / or position the acceleration sensor in a predefined manner. Particularly in order to remind to user of placing the acceleration sensor correctly, the user may be requested to confirm that the acceleration sensor is placed in the orientate and / or position. Here, a dedicated user confirmation action is required. Said user confirmation may be wrong.In a second method, the placement may be controlled by a designated check requesting the user to carry out specific actions, such as for a bracelet the user may be requested to raise the left and / or right arm. Again, dedicated user actions are required to determine the correct placement. This may introduce an extra initial step prior to each usage of the acceleration sensor. Additionally, these user actions might be carried out in a wrong way, leading to a wrong result.US9632981B2 discloses a method and system for calibrating a wireless sensor device. In a first aspect, the method comprises determining a vertical calibration vector and determining a rotation matrix using the vertical calibration vector to line up native axes of the wireless sensor device with body axes. In a second aspect, a wireless sensor device comprises a processor and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to determine a vertical calibration vector and to determine a rotation matrix using the vertical calibration vector to line up native axes of the wireless sensor device with body axes.US10317427B2 discloses a method and system for calibrating a wireless sensor device. In a first aspect, the method comprises determining a vertical calibration vector and determining a rotation matrix using the vertical calibration vector to line up native axes of the wireless sensor device with body axes. In a second aspect, a wireless sensor device comprises a processor and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to determine a vertical calibration vector and to determine a rotation matrix using the vertical calibration vector to line up native axes of the wireless sensor device with body axes.EP3076858B1 discloses a motion monitoring system and a method of obtaining and analyzing motion data that uses an accelerometer or an image capture device to acquire subject motion data. The motion data may be obtained from an accelerometer applied to a chest of the subject, or from an image capture device configured to view the subject during the detected motion. The motion data is analyzed to distinguish between a seizure type of motionand a non-seizure type of motion, with the subject motion characterized by at least one of, and combinations of, motion amplitude or magnitude, motion period or frequency, motion bandwidth, subject position, and subject change in position over a time period of the detected motion. The system and method further includes the generation of an output in response to an identification of the seizure and non-seizure types of motion.EP3122250A1 discloses systems, devices, and methods for tracking abdominal orientation and activity for purposes of preventing or treating conditions of pregnancy or other types of medical conditions. In certain specific embodiments, the system, device, or method relates to identifying abdominal orientation risk values, calculating and updating a cumulative risk value, comparing the cumulative risk value to a threshold, and outputting a warning when the cumulative risk value crosses the threshold.US9307915B2 discloses a system and method for measuring vital signs (e.g. SYS, DIA, SpO2, heart rate, and respiratory rate) and motion (e.g. activity level, posture, degree of motion, and arm height) from a patient. The system features: (i) first and second sensors configured to independently generate time-dependent waveforms indicative of one or more contractile properties of the patient's heart; and (ii) at least three motion-detecting sensors positioned on the forearm, upper arm, and a body location other than the forearm or upper arm of the patient. Each motion-detecting sensor generates at least one time-dependent motion waveform indicative of motion of the location on the patient's body to which it is affixed. A processing component, typically worn on the patient's body and featuring a microprocessor, receives the time-dependent waveforms generated by the different sensors and processes them to determine: (i) a pulse transit time calculated using a time difference between features in two separate time-dependent waveforms, (ii) a blood pressure value calculated from the time difference, and (iii) a motion parameter calculated from at least one motion waveform.US 2016 / 0302715 Al relates to systems, devices and methods for the detection of compromised tissue perfusion and other issues affecting the health of a patient. The method comprises determining a patient’s orientation and / or position relative to a support surface such as a bed surface. The method uses a sensor comprising accelerometers the sensor being positioned on the patient’s chest. More accurate position and / or orientation data are achieved when calibrating the sensor either in the factory or by the end user in a test setting (para.

[0082] ). Furthermore, the accuracy may be increase by determining a correction or offset of an actual sensor position on the patient’s chest with respect to a rotational axis of the patient, e.g. to correct for a patient’s chest angle and / or for a horizontal deviation of the actual sensorposition from the patient’s vertical axis (rotational axis). The determination of the correction or offset includes the placing the patient in a known orientation.Problem to be solvedIt is therefore desirable to provide a computer-implemented method for determining a position of a wearable sensor patch in a horizontal direction, an evaluation unit for determining a position of a wearable sensor patch in a horizontal direction, a remote device, a computer program, a computer-readable storage medium and a medical system, which solve at least one of the problems mentioned above.In particular, it is desirable to provide a computer-implemented method for determining a position of a wearable sensor patch in a horizontal direction, an evaluation unit for determining a position of a wearable sensor patch in a horizontal direction, a remote device, a computer program, a computer-readable storage medium and a medical system that offers a simple and user-friendly approach to increase the data accuracy.SummaryThis problem is addressed by a computer-implemented method for determining a position of a wearable sensor patch in a horizontal direction, an evaluation unit for determining a position of a wearable sensor patch in a horizontal direction, a remote device, a computer program, a computer-readable storage medium and a medical system having the features of the independent claims. Advantageous embodiments which might be realized in an isolated fashion or in any arbitrary combinations are listed in the dependent claims as well as throughout the specification.In a first aspect, a computer-implemented method for determining a position of a wearable sensor patch in a horizontal direction, wherein the wearable sensor patch is positioned on a user’s chest, wherein the wearable sensor patch comprises at least one acceleration sensor configured for generating acceleration sensor data is disclosed. For this aspect, reference may be made to any disclosure herein, particularly to any definition, Embodiment and / or further aspect as disclosed elsewhere herein.The computer-implemented method for determining a position of a wearable sensor patch in a horizontal direction comprises the following steps, which may be performed in the given order. A different order, however, may also be feasible. Further, two or more of the methodsteps may be performed simultaneously. Thereby, the method steps may at least partly overlap in time. Further, the method steps may be performed once or repeatedly. Thus, one or more or even all of the method steps may be performed once or repeatedly. The method may comprise additional method steps, which are not listed herein.The term "computer-implemented" as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a method which is performed by using computer programming, and / or by using at least one computer and / or at least one computer network. Thus, as an example, one or more or even all of the method steps may be performed by appropriate software, e.g. by using computer- readable instructions which, when executed on a computer or a computer network, cause the computer or computer network to perform the method steps. The term “software” as used herein may, specifically, refer to a computer program. The computer program may have a plurality of functions, procedures, methods and subprograms, which may be distributed over several specific hardware instances. The computer and / or computer network may comprise at least one processor, which is configured for performing at least one, more than one or all of the method steps of the method according to the present disclosure. The computer and / or computer network may comprise at least one memory configured for storing instruction, such as instructions related to the computer-implemented method. Specifically, each of the method steps is performed by the computer and / or computer network. The method may be performed completely automatically, specifically without user interaction.The term “wearable sensor patch” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a sensor patch that is configured to be “body -worn” by the user. Consequently, the sensor patch that may be in contact with the body of the user, particularly during an active wear time of the sensor patch. During the active wear time, the sensor patch may generate sensor data related to the user, such as acceleration sensor data in order to determine a gyrocardiogram of the user. The sensor patch may be configured to be mounted on a skin site of a body part selected from the group consisting of: an arm, exemplarily an upper arm; a stomach; a shoulder; a back; hip; a leg. Preferably, the body part may be a chest of the user. However, also other applications may be feasible. The wearable sensor patch may further comprise a surface, such as a flat or plane surface, configured for being placed on a user’s skin. The surface may be an adhesive surface. Specifically, the sensor patch may comprise an adhesive surface for attachment to the user’s skin. Alternatively or in addition, the body -worn sensorpatch may be attached to the body, specifically the chest, by using a strap, such as a chest strap. The term “adhesive surface” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically relates to a surface being capable to bind to an object and to resist separation. Exemplarily, the adhesive surface may comprise a plaster or an adhesive strip. The plaster or the adhesive strip may comprise an adhesive material.The wearable sensor patch may be positioned on a user’s chest, preferably, during an active wear time of the sensor patch. The term “user” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term exemplarily relates to a person intending to monitor an analyte value, such as a glucose value, in a person’s body tissue. In an embodiment, the term specifically may refer, without limitation, to a person using the medical device. For example, the user may be a patient suffering from a disease, such as diabetes.The method comprises:- receiving the acceleration sensor data, wherein the acceleration sensor data is related to a walking movement of the user,- wherein the acceleration sensor data comprises data related to the user walking in a forward or a backward direction during at least one left turn of the user and at least one right turn of the user- determining at least one left turn of the user and at least one right turn of the user, preferably while the user is moving in a forward or a backward direction, by evaluating the acceleration sensor data and, thereby, generating turn data, and- determining a horizontal position of the wearable sensor patch on the user’ s chest by evaluating the turn data, preferably wherein the horizontal position is determined relative to a horizontal center of the user’s chest,- wherein comparing the acceleration sensor data related to the at least one left turn and the acceleration sensor data related to the at least one right turn to each other is performed in order to determine an asymmetry in the turn data that is introduced due to a positioning of the wearable sensor patch in a horizontal direction that does not coincide with the horizontal center of the user’s chest.As already indicated, the method comprises receiving the acceleration sensor data, wherein the acceleration sensor data is related to a movement of the user, in particular, to a walking movement of the user.The term “receiving” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a process of getting access to and / or possession of data, specifically a receiving device may get access to and / or possession of the data. The data may be, specifically, sensor data generated by a sensor. The data may be transmitted by a transmitting device, specifically a sensor, configured for allowing access to the data. For allowing access to the data, the transmitting device may exchange the data with the receiving device, specifically an evaluation unit, such as by providing and / or sending the data. Consequently, the transmitting device may be a sending device and / or a measurement device. For receiving the data, the data may be requested by the receiving device, such as by sending or transferring a query to the transmitting device. Receiving the data may comprise a step of requesting the transmission of the data.The term “acceleration sensor” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a sensor configured to measure the acceleration of the sensor and, thereby, generate acceleration sensor data. The measured acceleration may be a static acceleration, such as gravity, or a dynamic acceleration, such as an acceleration caused by a movement, in particular, by a walking movement. The acceleration sensor may be a capacitive accelerometer, a piezoelectric accelerometer and / or a micro-electromechanical system accelerometer.The term “data” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to at least one discrete or continuous value that conveys information. Typically, data may comprise a plurality of said discrete or continuous values.The term “sensor data” may be data that is recorded and / or generated by a sensor. The sensor data as disclosed herein may be preexisting data in respect to performing the method for determining a position of a wearable sensor patch in a horizontal direction. The term “preexisting dataset” may indicate that the method for determining a position of a wearablesensor patch in a horizontal direction, typically, is free of a step of a data acquisition from the user. Consequently, the sensor data may be generated before the method for determining a position of a wearable sensor patch in a horizontal direction is performed. Consequently, the analyte sensor data may already exist at the time the method for determining a position of a wearable sensor patch in a horizontal direction receives the sensor data. The evaluation unit that is performing the method for determining a position of a wearable sensor patch in a horizontal direction may be different from the wearable sensor patch. The evaluation unit that is performing the method for determining a position of a wearable sensor patch in a horizontal direction may be a remote device. As a result, the data acquisition from the user may be performed be a device that is different from the device that is performing the method for determining a position of a wearable sensor patch in a horizontal direction.The term “acceleration sensor data” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to data that comprises information on the measured acceleration of the acceleration sensor. The acceleration sensor data may be generated by the acceleration sensor. The received acceleration sensor data may comprise data related to three acceleration vectors, wherein each acceleration vector is parallel to a different acceleration axis; wherein each acceleration axis is orthogonal to any one of the further acceleration axis.The method may comprise a step a generating the acceleration sensor data by using the acceleration sensor. Generating the acceleration sensor data may comprise performing a measurement with the acceleration sensor.The term "walking movement" as used herein is a broad term and is particularly used to describe a process of the user changing his or her position and / or location, in particular, a user’s movement that result in a translational change of position and location while the at least the user’s chest is positioned in a more or less upright position. The term specifically may refer, without limitation, to a process of changing a position and / or a location by walking, running or using any form of transportation such as a wheelchair, a scooter, a bike, a car etc. A walking movement may, consequently, comprise a phase in which an acceleration occurs.The invention may use walking movement data of a user wearing the chest patch and use comprised data on left-turns and right-turns to determine the position of the sensor patch on the user’ s chest. According to the invention, the position may be determined based on turning movements that may be extracted from walking movement data. No extra calibration process may be necessary.As a preferred solution, an automatic sensor position / placement detection may be proposed: By recording normal user activities, such as walking, for a defined period of time, such as 30 min, the placement of the sensor patch comprising an orientation and a position of the sensor patch on the body of the user, specifically a horizontal position, may be determined. Furthermore, it may be monitored whether the patch is removed from the body and replaced.For ease of algorithm design, x',y',z' 'normalized standard coordinates may be generated as the initial pre-processing step to run any further algorithms on these normalized standard coordinates that are independent of the concrete placement and precise at the same time.Of particular interest may be a movement that is related to a user or “movement of the user“. The movement that is related to a user that are related to a user may comprise or may be a movement that is related to a movement of the chest of the user. The movement of the chest of the user may be caused by a movement of at least a portion of a heart of the user. Consequently, the movement of the chest of the user may be analyzed in order to derive a gyrocardiogram of the heart of the user. Said movement may be an internal motion within a system defined by the user. Said movement may be referred to as a “micro-movement”. Alternatively or in addition, the movement that is related to a user that are related to a user may comprise or may be a movement that is related to a movement of the user having the effect that the user changes position, e.g. by walking and / or turning. The movement having the effect that the user changes position may be a translation and / or rotation of the user. Said movement may be an external motion of the system defined by the user. Said movement may be referred to as a “macro-movement”.In order of the acceleration sensor data being related to a movement of the user, the acceleration sensor data may comprise information on the movement that is related to a user, preferably information on a movement of the chest of the user caused by a movement of at least a portion of a heart of the user. Alternatively or in addition, in order of the acceleration sensor data being related to a walking movement of the user, the acceleration sensor data may comprise information on the movement that is related to a user, preferably information on a movement of the user having the effect that the user changes his or her overall position.As already indicated, the method comprises determining at least one left turn of the user and at least one right turn of the user, preferably while the user is walking, i.e. moving in a forward or a backward direction, by evaluating the acceleration sensor data and, thereby, generating turn data.The term "determining" as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a process of generating at least one representative result, in particular, by evaluating input data, such as the received analyte sensor data and / or the received event data. The at least one representative result may be the prediction time interval. The term determining may refer to the computer assisted processing of data, particularly in order to generate the at least one representative result. The at least one representative result may be generated in such a manner that it is available or provided as data or at least one item of information on the representative result. The term “information” may indicate that the representative result is described by the data. Generating the at least one representative result may be performed by using a computer program running on a computer or a computer network.The term "turn" as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a rotation of at least a portion of an object, preferably the user, and / or the entire object around a rotation axis. The rotation axis may be enclosed by the object or outside of the object. The rotation of the object may be superimposed by a translational motion of the object, particularly a forward or backward movement of the object when walking. A determined turn may be a turn that is performed when the user is standing upright. Additionally, a determined turn may be a turn that is performed when the user, preferably the chest of the user, is facing forward. The rotational axis of the turn may be parallel to a vertical axis related to the user. The "left turn" may be a specific rotation of at least a portion of an object in a specific direction, such as a left direction. The "right turn" may be a further specific rotation of at least a portion of an object in a further specific direction, such as a right direction. The specific direction may be opposite to the further specific direction.The term "turn data" as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special orcustomized meaning. The term specifically may refer, without limitation, to data that comprises information on the at least one left turn and at least one right turn of the user. The turn data may comprise the acceleration sensor data for the at least one left turn and at least one right turn of the user, preferably in a manner that the turn data comprises further information indicating which portion of the comprised acceleration sensor data is related to the at least one left turn and which portion of the comprised acceleration sensor data is related to at least one right turn of the user.Determining the at least one left turn and the at least one right turn may comprise o identifying the at least one left turn within the acceleration sensor data by comparing the acceleration sensor data to at least one known acceleration pattern related to at least one left turn; o identifying the at least one right turn within the acceleration sensor data by comparing the acceleration sensor data to at least one known acceleration pattern related to at least one right turn.The term “identifying” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a process of establishing and / or recognizing the identity or nature of someone or something. Identifying comprises comparing an object or a concept to specific known characteristics that distinguish the object or the concept from further objects or further concepts. Identifying may further comprise linking the object or the concept to a known identity attributed with the specific known characteristics when the specific known characteristics is found in the object or the concept.For identifying the at least one left turn within the acceleration sensor data, the at least one left turn within the acceleration sensor data may be compared to at least one known acceleration pattern related to at least one left turn. The portion of the acceleration sensor data that shows the at least one known acceleration pattern related to at least one left turn is then linked to a left turn.For identifying the at least one right turn within the acceleration sensor data, the at least one left turn within the acceleration sensor data may be compared to at least one known acceleration pattern related to at least one right turn. The portion of the acceleration sensor data that shows the at least one known acceleration pattern related to at least one right turn is then linked to a right turn.Alternatively or in addition, the at least one left turn within the acceleration sensor data and / or the at least one right turn within the acceleration sensor data may be identified with further sensor data, such as gyroscope sensor data. The respective turn within the acceleration sensor data may then be identified, by- determining a respective turn by evaluating the gyroscope sensor data by comparing the gyroscope sensor data to at least one known gyroscope pattern related to the respective turn; and- attributing the acceleration sensor data, which is generated at the same time in which the respective turn is determined in the gyroscope sensor data, to the to the respective turn.As already indicated, the method comprises determining a horizontal position of the wearable sensor patch on the user’s chest by evaluating the turn data, preferably wherein the horizontal position is determined relative to a horizontal center of the user’s chest.The term “position" as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a specific place or location. A position may be given in a coordinate system on an axis.Of particular interest may be a “horizontal position”. The human body may define a “horizontal axis”. Said horizontal axis may, alternatively, be referred to as frontal axis. The horizontal axis may be defined by an intersection of a frontal plane and a transverse plane that are further defined by the human body. The horizontal axis may be parallel to a ground and / or a floor when a human is standing in an anatomical position. In addition, the horizontal axis may run from the left side of the human to the right side of the human. In the anatomical position the human is standing upright, the human is facing forward, the human has the arms at the sides and the human has the palms facing forward. A “horizontal position” may refer to a specific position on the horizontal axis.For the horizontal position being determined relative to a horizontal center of the user’s chest, the horizontal center of the human chest may define a reference point for the determined relative position. In order to do so, the horizontal center may define the origin of the horizontal axis.The term “horizontal center” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to aspecial or customized meaning. The term specifically may refer, without limitation, to a position of a center of a human chest on the horizontal axis. The center of the human chest on the horizontal axis may be on a sagittal plane that is defined by the human body.Determining the horizontal position of the wearable sensor patch on the user’s chest by evaluating the turn data may comprise o comparing the acceleration sensor data related to the at least one left turn and the acceleration sensor data related to the at least one right turn to each other.Comparing the acceleration sensor data related to the at least one left turn and the acceleration sensor data related to the at least one right turn to each other may be performed in order to determine an asymmetry in the turn data that is introduced due to a positioning of the wearable sensor patch, specifically the acceleration sensor, in a horizontal direction that does not coincide with the horizontal center of the user’s chest. This asymmetry may be caused due to a deviating distance between the acceleration sensor and the rotational axis of the turn.Comparing the acceleration sensor data related to the at least one left turn and the acceleration sensor data related to the at least one right turn to each other may comprise o determining at least one rotation for the at least one left turn by evaluating the turn data; o determining at least one rotation for the at least one right turn by evaluating the turn data.The term “rotation” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to the movement of an object around a central point or a rotation axis. The rotation may be or may be related to a movement on a circular path, an elliptical path and / or a further curved trajectories. Determining at least one rotation may comprise determining how the object has rotated, such as by determining at least one of an angle, an angular velocity, an angular acceleration, a length of the curved trajectory. Gyroscope sensor data may further be evaluated in order to determine the at least one rotation.Comparing the acceleration sensor data related to the at least one left turn and the acceleration sensor data related to the at least one right turn may compriseo comparing the determined at least one rotation for the at least one left turn and the determined at least one rotation for the at least one right turn to each other in order to determine the horizontal position of the wearable sensor patch on the user’s chest, preferably wherein the horizontal position is determined relative to the horizontal center of the user’s chest.As already indicated, comparing the acceleration sensor data related to the at least one left turn and the acceleration sensor data related to the at least one right turn to each other may be performed in order to determine an asymmetry in the turn data that is introduced due to a positioning of the wearable sensor patch, specifically the acceleration sensor, in a horizontal direction that does not coincide with the horizontal center of the user’s chest. By comparing the at least one rotation for the at least one left turn and the at least one rotation for the at least one right turn, said asymmetry in the turn data may be determined. From this asymmetry the horizontal position wearable sensor patch may be derived.Comparing the determined at least one rotation for the at least one left turn and the determined at least one rotation for the at least one right turn to each other may comprise o determining a turning angle of the at least one left turn and a turning angle of the at least one right turn, wherein the turning angle is the angle between an initial direction of movement before making the respective turn and a final direction of movement after making the respective turn.The at least one rotation for the at least one left turn and the at least one rotation for the at least one right turn that are compared to each may be selected to have a difference between the absolute value of the determined turning angle of the at least one left turn and the absolute value of the determined turning angle of the at least one right turn below a threshold.The term “turning angle” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an angle between an initial direction of forward or backward movement before making the respective turn and a final direction of forward or backward movement after making the respective turn. The movement before making the respective turn may be a linear movement along a straight line, preferably a non-curved movement. The movement after making the respective turn may be a linear movement along a straight line, a non-curved movement.The term “selected to have” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a specialor customized meaning. The term specifically may refer, without limitation, to a process of choosing turns from a plurality of turns based on at least one criteria. The chosen turns may be compared to each other.The respective turning angle may be determined by using a gyroscope, wherein, for using the gyroscope, gyroscope sensor data generated by the gyroscope is evaluated in order to determine the respective turning angle.The term “gyroscope” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a device configured for measuring at least one angular velocity. The gyroscope sensor data may be generated by at least one angular velocity. The gyroscope sensor data may comprise information on the at least one measured orientation and / or the at least one angular velocity. The gyroscope sensor data may be generated by using a gyroscope. Further, an orientation may be determined by using an Inertial Measurement Unit. The Inertial Measurement Unit may be comprised by the wearable sensor patch.Comparing the determined at least one rotation for the at least one left turn and the determined at least one rotation for the at least one right turn to each other may comprise o determining a turn length of the at least one left turn and a turn length of the at least one right turn, wherein a turn length is a distance that the user traveled while making the respective turn.The at least one rotation for the at least one left turn and the at least one rotation for the at least one right turn that are compared to each other may be selected to have a difference between the absolute value of the determined turn length of the at least one left turn and the absolute value of the determined turn length of the at least one right turn below a threshold.The term “turn length” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a distance of a turn.The respective turn length may be determined by using at least one of o a GPS sensor, wherein, for using the GPS sensor, GPS sensor data generated by the GPS sensor is evaluated in order to determine the respective turn length;o the acceleration sensor, wherein, for using the acceleration sensor, the acceleration sensor data is evaluated in order to determine the respective turn length; o an Ultra Wideband module, wherein, for using the Ultra Wideband module, Ultra Wideband module data generated by the Ultra Wideband module is evaluated in order to determine the respective turn length.The turn length may be determined by using the acceleration sensor by integrating the acceleration sensor data related to the turn two times in order to derive the respective turn length. Ultra-wideband, or UWB, may be considered a short-range technology for wireless communication that may be leveraged to detect a location. An Ultra Wideband module may allow a device to send data over short ranges to a further Ultra Wideband module. Alternatively or in addition, an Ultra Wideband module may also be used to sense the location of the transmitting Ultra Wideband module. This makes it possible to determine the location of a device, specifically the wearable sensor patch, comprising the Ultra Wideband module.Comparing the determined at least one rotation for the at least one left turn and the determined at least one rotation for the at least one right turn to each other may comprise o determining round trip data on a round trip of the user comprising an outward path and a return path, wherein the outward path and the return path at least partially overlap and, thereby, define an at least partial overlap, o determining at least one pair of recurrent turns on the at least partial overlap of the outward path and the return path, wherein the at least one pair of recurrent turns comprises a specific left turn and a right turn, wherein the specific left turn and the specific right turn correspond to the same turn on the at least partial overlap.The at least one rotation for the at least one left turn and the at least one rotation for the at least one right turn that may be compared to each other may be selected to be the specific left turn and the specific right turn comprised by the at least one pair of recurrent turns.The round trip data may be determined by using at least one of: o the acceleration sensor, wherein, for using the acceleration sensor, the acceleration sensor data generated by the acceleration sensor is evaluated in order to find at least one recurring pattern in the acceleration sensor data indicating a round trip; o the GPS sensor, wherein, for using the GPS sensor, GPS sensor data generated by the GPS sensor is evaluated;o the gyroscope, wherein, for using the gyroscope, gyroscope sensor data generated by the gyroscope is evaluated in order to determine the respective turning angle wherein, for using the movement pattern, the movement pattern is evaluated in order to determine the respective turning angle of a specific turn in the outward path and the respective turning angle of the specific turn in the return path.The term “GPS sensor” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an arbitrary device configured to receive signals from satellites in order to determine a geoposition of the GPS sensor.The horizontal position of the wearable sensor patch on the user’s chest may be determined by using at least one algorithm configured to determine the horizontal position of the wearable sensor patch on the user’s chest, wherein the algorithm configured to determine the horizontal position of the wearable sensor patch on the user’s chest may comprise at least one of:■ a look-up table;■ a machine learning model;■ an analytical model.The term “machine learning model” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a mathematical model which is trainable on at least one training dataset using machine learning, in particular deep learning or other forms of artificial intelligence. The term “machine learning” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a method of using artificial intelligence (Al) for automated model building. The training may be performed using at least one machine-learning system. The term “machine-learning system” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a system or unit comprising at least one processing unit such as a processor, microprocessor, or computer system configured for machine learning, in particular for executing a logic in a given algorithm. The machine-learningsystem may be configured for performing and / or executing at least one machine-learning algorithm, wherein the machine-learning algorithm is configured for generating the trained machine learning model.The method further may comprise:- determining a gyrocardiogram of the user by evaluating the acceleration sensor data and the horizontal position of the wearable sensor patch on the user’s chest.The term “gyrocardiogram” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a report on at least one characteristic on at least one rotation and / or translation related to the heart, preferably of the user. A gyrocardiogram may comprise information on at least one heart rotational movement, at least one cardiac cycle phase; at least one mechanical function and dynamics; at least one cardiac health indicator.The gyrocardiogram of the user may be determined by using at least one algorithm configured to determine the gyrocardiogram of the user, wherein the algorithm configured to determine the gyrocardiogram of the user may comprise at least one of■ a look-up table;■ a machine learning model;■ an analytical model.Determining a gyrocardiogram of the user by evaluating the acceleration sensor data and the horizontal position of the wearable sensor patch on the user’s chest may comprise o compensating an influence of the determined horizontal position of the wearable sensor patch on the user’s chest on the gyrocardiogram of the user.The term “compensating” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a process of taking action to offset, counterbalance and / or mitigate an effect and, thereby, reducing or completely neutralizing the impact of the effect. When compensating an influence of the determined horizontal position of the wearable sensor patch on the user’s chest on the gyrocardiogram of the user, the influence of the horizontal position on the gyrocardiogram may be at least reduced and / or fully neutralized.Compensating an influence of the determined horizontal position of the wearable sensor patch on the user’s chest on the acceleration sensor data may comprise at least one of: o correcting the acceleration sensor data; o selecting a specific algorithm configured to determine the gyrocardiogram of the user.The method may comprise:- transferring the acceleration sensor data generated in a known local coordinate system of the acceleration sensor into a world coordinate system by evaluating a transformation matrix, preferably wherein data related to or based on the transferred acceleration sensor data is evaluated when determining the gyrocardiogram of the user.In the context of coordinate systems, the term “transferring” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to refers to converting coordinates from one coordinate system to a further coordinate system.The acceleration sensor data may be further related to a gravitational acceleration. The transformation matrix may be determined by o determining a first axis of the world coordinate system by evaluating at least one gravitational acceleration vector within the acceleration sensor data, wherein the first axis is aligned parallel to the gravitational acceleration vector, preferably wherein the first axis is aligned in a manner that said first axis is pointing into a direction of the gravitational acceleration vector.Determining the first axis of the world coordinate system may comprise o identifying the at least one gravitational acceleration vector within the acceleration sensor data by comparing the acceleration sensor data to at least one known absolute value of the gravitational acceleration.The transformation matrix may be determined by o determining a second axis of the world coordinate system by evaluating a straight forward movement vector within the acceleration sensor data, whereinthe Second axis is aligned parallel to the straight forward movement vector, preferably wherein the Second axis is aligned in a manner that said Second axis is pointing into a direction of the straight forward movement vector.The straight forward movement vector may be determined while the user is walking in order to ensure that the user is in an upright position. By evaluating the determined first axis of the world coordinate system and the determined second axis of the world coordinate system, a rotation of the sensor patch around its vertical axis may be determined.Determining a second axis of the world coordinate system may comprise o identifying the at least one straight forward movement vector within the acceleration sensor data by comparing the acceleration sensor data to at least one known movement pattern related to at least one straight forward movement vector, preferably and by taking the first axis of the world coordinate system into account.Further, when identifying the at least one straight forward movement vector within the acceleration sensor data, an assumed initial pose of the wearable sensor patch may be taken into account. Alternatively or in addition, when identifying the at least one straight forward movement vector within the acceleration sensor data, an assumed initial pose of the wearable sensor patch may be taken into account. Alternatively or in addition, when identifying the at least one straight forward movement vector within the acceleration sensor data, an orientation invariant feature may be evaluated in order to determine that the user is walking. The term “orientation invariant feature” may refer to a feature indicating a movement of the user, wherein the feature may be independent from the pose of the acceleration sensor. The orientation invariant feature may be an orientation invariant summed feature, such as a sum of acceleration values in x, y and z, wherein x, y and z are given in the coordinate system of the acceleration sensor. Said sum may be independent of the position of x, y, z in the world coordinate system of the acceleration sensor, but may show a typical pattern while the user is walking. Said sum may, therefore, be used to detect that the user is walking, preferably without having to make further assumptions about the position of the acceleration sensor on the user’s chest. Alternatively or in addition, when identifying the at least one straight forward movement vector within the acceleration sensor data, identifying the straight forward movement vector may comprise identifying an upright posture of the user based on the determined first axis of the world coordinate system and / or the identified at least one gravitational acceleration vector. Said movement may be indicated a movement pattern with the acceleration sensor data.The transformation matrix may be determined byo determining a third axis of the world coordinate system by evaluating the Second axis and the first axis, wherein the third axis of the world coordinate system is aligned orthogonal to the Second axis, wherein the third axis of the world coordinate system is aligned orthogonal to the first axis, wherein, by determining the third axis of the world coordinate system, the known world coordinate system is derived.The transformation matrix may be determined by o determining the transformation matrix by evaluating the known local coordinate system and the known world coordinate system.In a further aspect, an evaluation unit for determining a position of a wearable sensor patch in a horizontal direction is disclosed, wherein the evaluation unit is configured for performing the computer-implemented method for determining a position of a wearable sensor patch in a horizontal direction as elsewhere described herein. For this aspect, reference may be made to any disclosure herein, particularly to any definition, Embodiment and / or further aspect as disclosed elsewhere herein.The term “evaluation unit” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an arbitrary device or an arbitrary system of device configured for determining a position of a wearable sensor patch.The evaluation unit, for being configured for performing the computer-implemented method as elsewhere described herein, may comprise a processor and a memory with instructions, which when executed cause the processor to perform the computer-implemented method as elsewhere disclosed herein.The term “processor” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an arbitrary logic circuitry configured for performing basic operations of a computer or system, and / or, generally, to a device which is configured for performing calculations or logic operations. In particular, the processor may be configured for processing basic instructions that drive the computer or system. As an example, the processor may comprise at least one arithmeticlogic unit (ALU), at least one floating-point unit (FPU), such as a math co-processor or a numeric co-processor, a plurality of registers, specifically registers configured for supplying operands to the ALU and storing results of operations, and a memory, such as an LI and L2 cache memory. In particular, the processor may be a multi -core processor. Specifically, the processor may be or may comprise a central processing unit (CPU). Additionally or alternatively, the processor may be or may comprise a microprocessor, thus specifically the processor’s elements may be contained in one single integrated circuitry (IC) chip. Additionally or alternatively, the processor may be or may comprise one or more application-specific integrated circuits (ASICs) and / or one or more field-programmable gate arrays (FPGAs) and / or one or more tensor processing unit (TPU) and / or one or more chip, such as a dedicated machine learning optimized chip, or the like. The processor specifically may be configured, such as by software programming, for performing one or more evaluation operations as will be outlined in further detail below.The evaluation unit may be comprised by at least one remote device, wherein the remote device is physically separate from the wearable sensor patch.In a further aspect, a remote device is disclosed, wherein the remote device comprises at least one evaluation unit as elsewhere described herein, wherein the remote device is physically separate from the wearable sensor patch. For this aspect, reference may be made to any disclosure herein, particularly to any definition, Embodiment and / or further aspect as disclosed elsewhere herein.The term “remote device” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an arbitrary device or system of devices separate and distant from the wearable sensor patch, particularly in a manner that the remote device and the wearable sensor patch comprise at least two different and separated housings. A data transfer between the remote device and the wearable sensor patch may be a wireless data transfer. The data transfer may be via transmitter.The remote device may be or may comprised by a mobile device. The term “mobile device” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a mobile electronics device, specifically a personal mobile device (PDA), more specifically to a mobile communication device such as a cell phone and / or a smartphone. Additionally or alternatively, the mobile devicemay also refer to a notebook, a tablet computer or another type of portable computer, such as a wearable, specifically smart glasses. The mobile device may comprise a transmitter. Thus, generally, the mobile devices may be selected from the group consisting of: a cell phone having at least one camera, specifically a smart phone; a portable computer having at least one camera, specifically at least one of a notebook and a tablet computer.In a further aspect, a computer program comprising instructions is disclosed which, when the program is executed by the evaluation unit as elsewhere disclosed herein, cause the evaluation unit to perform the computer-implemented method as elsewhere disclosed herein. For this aspect, reference may be made to any disclosure herein, particularly to any definition, Embodiment and / or further aspect as disclosed elsewhere herein.In a further aspect, a computer-readable storage medium comprising instructions is disclosed which, when the instructions are executed by the evaluation unit as elsewhere disclosed herein cause the evaluation unit to perform the computer-implemented method as elsewhere disclosed herein. For this aspect, reference may be made to any disclosure herein, particularly to any definition, Embodiment and / or further aspect as disclosed elsewhere herein.As used herein, the terms “computer-readable data carrier” and “computer-readable storage medium” specifically may refer to non-transitory data storage means, such as a hardware storage medium having stored thereon computer-executable instructions. The computer- readable data carrier or storage medium specifically may be or may comprise a storage medium such as a random-access memory (RAM) and / or a read-only memory (ROM).In a further aspect, a non-transient computer-readable medium comprising instructions is disclosed which, when the instructions are executed by the evaluation unit as elsewhere disclosed herein cause the evaluation unit to perform the computer-implemented method a as elsewhere disclosed herein. For this aspect, reference may be made to any disclosure herein, particularly to any definition, Embodiment and / or further aspect as disclosed elsewhere herein.In a further aspect, a medical system is disclosed, the medical system comprises a. at least one wearable sensor patch configured for being positioned on a user’s chest, wherein the wearable sensor patch comprises at least one acceleration sensor configured for generating acceleration sensor data, wherein the at least one wearable sensor patch is further configured for transmitting the acceleration sensor data to at least one evaluation unit by using a connection interface;b. the at least one evaluation unit as elsewhere disclosed herein, wherein the at least one evaluation unit is further configured for receiving the acceleration sensor data transmitted by the wearable sensor patch by using a connection interface.For this aspect, reference may be made to any disclosure herein, particularly to any definition, Embodiment and / or further aspect as disclosed elsewhere herein.The medical system further may comprise c. at least one remote device, wherein the remote device comprises the evaluation unit, wherein the remote device is physically separate from the wearable sensor patch.Further disclosed and proposed herein is a computer program including computer-executable instructions for performing the method according to the present invention in one or more of the embodiments enclosed herein when the instructions are executed on a computer or computer network. Specifically, the computer program may be stored on a computer-readable data carrier and / or on a computer-readable storage medium.Thus, specifically, one, more than one or even all of method steps a) to d) as indicated above may be performed by using a computer or a computer network, preferably by using a computer program.Further disclosed and proposed herein is a computer program product having program code means, in order to perform the method according to the present invention in one or more of the embodiments enclosed herein when the program is executed on a computer or computer network. Specifically, the program code means may be stored on a computer-readable data carrier and / or on a computer-readable storage medium.Further disclosed and proposed herein is a data carrier having a data structure stored thereon, which, after loading into a computer or computer network, such as into a working memory or main memory of the computer or computer network, may execute the method according to one or more of the embodiments disclosed herein.Further disclosed and proposed herein is a non-transient computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to perform a method as elsewhere disclosed herein.Further disclosed and proposed herein is a computer program product with program code means stored on a machine-readable carrier, in order to perform the method according to one or more of the embodiments disclosed herein, when the program is executed on a computer or computer network. As used herein, a computer program product refers to the program as a tradable product. The product may generally exist in an arbitrary format, such as in a paper format, or on a computer-readable data carrier and / or on a computer-readable storage medium. Specifically, the computer program product may be distributed over a data network.Finally, disclosed and proposed herein is a modulated data signal which contains instructions readable by a computer system or computer network, for performing the method according to one or more of the embodiments disclosed herein.Referring to the computer-implemented aspects of the invention, one or more of the method steps or even all of the method steps of the method according to one or more of the embodiments disclosed herein may be performed by using a computer or computer network. Thus, generally, any of the method steps including provision and / or manipulation of data may be performed by using a computer or computer network. Generally, these method steps may include any of the method steps, typically except for method steps requiring manual work, such as providing the samples and / or certain aspects of performing the actual measurements.Specifically, further disclosed herein are:- a computer or computer network comprising at least one processor, wherein the processor is adapted to perform the method according to one of the embodiments described in this description,- a computer loadable data structure that is adapted to perform the method according to one of the embodiments described in this description while the data structure is being executed on a computer,- a computer program, wherein the computer program is adapted to perform the method according to one of the embodiments described in this description while the program is being executed on a computer,- a computer program comprising program means for performing the method according to one of the embodiments described in this description while the computer program is being executed on a computer or on a computer network,- a computer program comprising program means according to the preceding embodiment, wherein the program means are stored on a storage medium readable to a computer,- a storage medium, wherein a data structure is stored on the storage medium and wherein the data structure is adapted to perform the method according to one of the embodiments described in this description after having been loaded into a main and / or working storage of a computer or of a computer network, and- a computer program product having program code means, wherein the program code means can be stored or are stored on a storage medium, for performing the method according to one of the embodiments described in this description, if the program code means are executed on a computer or on a computer network.As used in the following, the terms “have”, “comprise” or “include” or any arbitrary grammatical variations thereof are used in a non-exclusive way. Thus, these terms may both refer to a situation in which, besides the feature introduced by these terms, no further features are present in the entity described in this context and to a situation in which one or more further features are present. As an example, the expressions “A has B”, “A comprises B” and “A includes B” may both refer to a situation in which, besides B, no other element is present in A (i.e. a situation in which A solely and exclusively consists of B) and to a situation in which, besides B, one or more further elements are present in entity A, such as element C, elements C and D or even further elements.Further, it shall be noted that the terms “at least one”, “one or more” or similar expressions indicating that a feature or element may be present once or more than once typically will be used only once when introducing the respective feature or element. In the following, in most cases, when referring to the respective feature or element, the expressions “at least one” or “one or more” will not be repeated, non-withstanding the fact that the respective feature or element may be present once or more than once.Further, as used in the following, the terms "preferably", "more preferably", "particularly", "more particularly", "specifically", "more specifically" or similar terms are used in conjunction with optional features, without restricting alternative possibilities. Thus, features introduced by these terms are optional features and are not intended to restrict the scope of the claims in any way. The invention may, as the skilled person will recognize, be performed by using alternative features. Similarly, features introduced by "in an embodiment of the invention" or similar expressions are intended to be optional features, without any restriction regarding alternative embodiments of the invention, without any restrictions regarding the scope of the invention and without any restriction regarding the possibility of combining the features introduced in such way with other optional or non-optional features of the invention.The proposed computer-implemented method for controlling a computer-implemented method for determining a position of a wearable sensor patch in a horizontal direction, evaluation unit for determining a position of a wearable sensor patch in a horizontal direction, remote device, computer program, computer-readable storage medium and medical system provide many advantages over known devices and methods.In particular, the proposed computer-implemented method for controlling a computer-implemented method for determining a position of a wearable sensor patch in a horizontal direction, evaluation unit for determining a position of a wearable sensor patch in a horizontal direction, remote device, computer program, computer-readable storage medium and medical system provide a simple and user-friendly approach to increase the data accuracy.The invention may be described in the following words:Computer-implemented method for determining a position of position of wearable sensor patch in a horizontal direction positioned on a user’s chest with the steps,- Receiving data related to a walking movement of the user as moving data,- Determining at least one left turn and at least one right turn within the moving data as turn data, and- Determining based on the turn data a horizontal position of the wearable sensor patch relative to the user’s chest.Summarizing and without excluding further possible embodiments, the following embodiments may be envisaged:Embodiment 1 : A computer-implemented method for determining a position of a wearable sensor patch in a horizontal direction, wherein the wearable sensor patch is positioned on a user’s chest, wherein the wearable sensor patch comprises at least one acceleration sensor configured for generating acceleration sensor data, the method comprising:- receiving the acceleration sensor data, wherein the acceleration sensor data is related to a walking movement of the user,- determining at least one left turn of the user and at least one right turn of the user, preferably while the user is moving in a forward or a backward direction, by evaluating the acceleration sensor data and, thereby, generating turn data, anddetermining a horizontal position of the wearable sensor patch on the user’ s chest by evaluating the turn data, preferably wherein the horizontal position is determined relative to a horizontal center of the user’s chest.Embodiment 2: The method according to the preceding Embodiment, wherein the received acceleration sensor data comprises data related to three acceleration vectors, wherein each acceleration vector is parallel to a different acceleration axis; wherein each acceleration axis is orthogonal to any one of the further acceleration axis.Embodiment 3 : The method according to any one of the preceding Embodiments, wherein determining the at least one left turn and the at least one right turn comprises o identifying the at least one left turn within the acceleration sensor data by comparing the acceleration sensor data to at least one known acceleration pattern related to at least one left turn; o identifying the at least one right turn within the acceleration sensor data by comparing the acceleration sensor data to at least one known acceleration pattern related to at least one right turn.Embodiment 4: The method according to any one of the preceding Embodiments, wherein determining the horizontal position of the wearable sensor patch on the user’s chest by evaluating the turn data comprises o comparing the acceleration sensor data related to the at least one left turn and the acceleration sensor data related to the at least one right turn to each other.Embodiment 5 : The method according to the preceding Embodiment, wherein comparing the acceleration sensor data related to the at least one left turn and the acceleration sensor data related to the at least one right turn to each other comprises o determining at least one rotation for the at least one left turn by evaluating the turn data; o determining at least one rotation for the at least one right turn by evaluating the turn data.Embodiment 6: The method according to the preceding Embodiment, wherein comparing the acceleration sensor data related to the at least one left turn and the acceleration sensor data related to the at least one right turn comprises o comparing the determined at least one rotation for the at least one left turn and the determined at least one rotation for the at least one right turn to each other inorder to determine the horizontal position of the wearable sensor patch on the user’s chest, preferably wherein the horizontal position is determined relative to the horizontal center of the user’s chest.Embodiment 7 : The method according to the preceding Embodiment, wherein comparing the determined at least one rotation for the at least one left turn and the determined at least one rotation for the at least one right turn to each other comprises o determining a turning angle of the at least one left turn and a turning angle of the at least one right turn, wherein the turning angle is the angle between an initial direction of movement before making the respective turn and a final direction of movement after making the respective turn; wherein the at least one rotation for the at least one left turn and the at least one rotation for the at least one right turn that are compared to each other are selected to have a difference between the absolute value of the determined turning angle of the at least one left turn and the absolute value of the determined turning angle of the at least one right turn below a threshold.Embodiment 8 : The method according to the preceding Embodiment, wherein the respective turning angle is determined by a gyroscope, wherein, for using the gyroscope, gyroscope sensor data generated by the gyroscope is evaluated in order to determine the respective turning angle.Embodiment 9: The method according to any one of the three preceding Embodiments, wherein comparing the determined at least one rotation for the at least one left turn and the determined at least one rotation for the at least one right turn to each other comprises o determining a turn length of the at least one left turn and a turn length of the at least one right turn, wherein a turn length is a distance that the user traveled while making the respective turn; wherein the at least one rotation for the at least one left turn and the at least one rotation for the at least one right turn that are compared to each other are selected to have a difference between the absolute value of the determined turn length of the at least one left turn and the absolute value of the determined turn length of the at least one right turn below a threshold.Embodiment 10: The method according to the preceding Embodiment, wherein the respective turn length is determined by using at least one of:o a GPS sensor, wherein, for using the GPS sensor, GPS sensor data generated by the GPS sensor is evaluated in order to determine the respective turn length; o the acceleration sensor, wherein, for using the acceleration sensor, the acceleration sensor data is evaluated in order to determine the respective turn length; o an Ultra Wideband module, wherein, for using the Ultra Wideband module, Ultra Wideband module data generated by the Ultra Wideband module is evaluated in order to determine the respective turn length.Embodiment 11 : The method according to any one of the five preceding Embodiments, wherein comparing the determined at least one rotation for the at least one left turn and the determined at least one rotation for the at least one right turn to each other comprises o determining round trip data on a round trip of the user comprising an outward path and a return path, wherein the outward path and the return path at least partially overlap and, thereby, define an at least partial overlap, o determining at least one pair of recurrent turns on the at least partial overlap of the outward path and the return path, wherein the at least one pair of recurrent turns comprises a specific left turn and a right turn, wherein the specific left turn and the specific right turn correspond to the same turn on the at least partial overlap, wherein the at least one rotation for the at least one left turn and the at least one rotation for the at least one right turn that are compared to each other are selected to be the specific left turn and the specific right turn comprised by the at least one pair of recurrent turns.Embodiment 12: The method according to the preceding Embodiment, wherein the round trip data is determined by using at least one of: o the acceleration sensor, wherein, for using the acceleration sensor, the acceleration sensor data generated by the acceleration sensor is evaluated in order to find at least one recurring pattern in the acceleration sensor data indicating a round trip; o the GPS sensor, wherein, for using the GPS sensor, GPS sensor data generated by the GPS sensor is evaluated; o the gyroscope, wherein, for using the gyroscope, gyroscope sensor data generated by the gyroscope is evaluated in order to determine the respective turning angle wherein, for using the movement pattern, the movement pattern is evaluated in order to determine the respective turning angle of a specific turn in theoutward path and the respective turning angle of the specific turn in the return pathEmbodiment 13: The method according to any one of the preceding Embodiments, wherein the horizontal position of the wearable sensor patch on the user’s chest is determined by using at least one algorithm configured to determine the horizontal position of the wearable sensor patch on the user’s chest, wherein the algorithm configured to determine the horizontal position of the wearable sensor patch on the user’s chest comprises at least one of:■ a look-up table;■ a machine learning model;■ an analytical model.Embodiment 14: The method according to any one of the preceding Embodiments, the method further comprising:- determining a gyrocardiogram of the user by evaluating the acceleration sensor data and the horizontal position of the wearable sensor patch on the user’s chest.Embodiment 15: The method according to the preceding Embodiment, wherein the gyrocardiogram of the user is determined by using at least one algorithm configured to determine the gyrocardiogram of the user, wherein the algorithm configured to determine the gyrocardiogram of the user comprises at least one of:■ a look-up table;■ a machine learning model;■ an analytical model.Embodiment 16: The method according to any one of the two preceding Embodiments, wherein determining a gyrocardiogram of the user by evaluating the acceleration sensor data and the horizontal position of the wearable sensor patch on the user’s chest comprises o Compensating an influence of the determined horizontal position of the wearable sensor patch on the user’s chest on the gyrocardiogram of the user.Embodiment 17: The method according to the preceding Embodiment, wherein Compensating an influence of the determined horizontal position of the wearable sensor patch on the user’s chest on the acceleration sensor data comprises at least one of: o correcting the acceleration sensor data;o selecting a specific algorithm configured to determine the gyrocardiogram of the user.Embodiment 18: The method according to any one of the preceding Embodiments, the method comprising:- transferring the acceleration sensor data generated in a known local coordinate system of the acceleration sensor into a world coordinate system by evaluating a transformation matrix, preferably wherein data related to or based on the transferred acceleration sensor data is evaluated when determining the gyrocardiogram of the user.Embodiment 19: The method according to the two preceding Embodiment, preferably wherein the acceleration sensor data is further related to a gravitational acceleration, wherein the transformation matrix is determined by o determining a first axis of the world coordinate system by evaluating at least one gravitational acceleration vector within the acceleration sensor data, wherein the first axis is aligned parallel to the gravitational acceleration vector, preferably wherein the first axis is aligned in a manner that said first axis is pointing into a direction of the gravitational acceleration vector.Embodiment 20: The method according to the preceding Embodiment, wherein determining the first axis of the world coordinate system comprises o identifying the at least one gravitational acceleration vector within the acceleration sensor data by comparing the acceleration sensor data to at least one known absolute value of the gravitational acceleration.Embodiment 21 : The method according to any one of the two preceding Embodiments, wherein the transformation matrix is determined by o determining a second axis of the world coordinate system by evaluating a straight forward movement vector within the acceleration sensor data, wherein the Second axis is aligned parallel to the straight forward movement vector, preferably wherein the second axis is aligned in a manner that said second axis is pointing into a direction of the straight forward movement vector.Embodiment 22: The method according to the preceding Embodiment, wherein determining a Second axis of the world coordinate system compriseso identifying the at least one straight forward movement vector within the acceleration sensor data by comparing the acceleration sensor data to at least one known movement pattern related to at least one straight forward movement vector, preferably and by taking the first axis of the world coordinate system into account.Embodiment 23 : The method according to any one of the two preceding Embodiments, wherein the transformation matrix is determined by o determining a third axis of the world coordinate system by evaluating the Second axis and the first axis, wherein the third axis of the world coordinate system is aligned orthogonal to the Second axis, wherein the third axis of the world coordinate system is aligned orthogonal to the first axis, wherein, by determining the third axis of the world coordinate system, the known world coordinate system is derived.Embodiment 24: The method according to the preceding Embodiment, wherein the transformation matrix is determined by o determining the transformation matrix by evaluating the known local coordinate system and the known world coordinate system.Embodiment 25: An evaluation unit for determining a position of a wearable sensor patch in a horizontal direction, wherein the evaluation unit is configured for performing the computer-implemented method for determining a position of a wearable sensor patch in a horizontal direction according to any one of the preceding method Embodiments.Embodiment 26: The evaluation unit according to the preceding Embodiment, wherein the evaluation unit, for being configured for performing the computer-implemented method according to any one of the preceding method Embodiments, comprises a processor and a memory with instructions, which when executed cause the processor to perform the computer-implemented method according to any one of the preceding method Embodiments.Embodiment 27: The evaluation unit according to any one of the two preceding Embodiments, wherein the evaluation unit is comprised by at least one remote device, wherein the remote device is physically separate from the wearable sensor patch.Embodiment 28: A remote device, wherein the remote device comprises at least one evaluation unit according to any one of the preceding Embodiments referring to an evaluation unit, wherein the remote device is physically separate from the wearable sensor patch.Embodiment 29: A computer program comprising instructions which, when the program is executed by the evaluation unit according to any one of the preceding Embodiments referring to an evaluation unit, cause the evaluation unit to perform the computer-implemented method according to any one of the preceding method Embodiments.Embodiment 30: A computer-readable storage medium comprising instructions which, when the instructions are executed by the evaluation unit according to any one of the preceding Embodiments referring to an evaluation unit cause the evaluation unit to perform the computer-implemented method according to any one of the preceding method Embodiments.Embodiment 31 : A non-transient computer-readable medium comprising instructions which, when the instructions are executed by the evaluation unit according to any one of the preceding Embodiments referring to an evaluation unit cause the evaluation unit to perform the computer-implemented method according to any one of the preceding method Embodiments.Embodiment 32: A medical system comprising a. at least one wearable sensor patch configured for being positioned on a user’s chest, wherein the wearable sensor patch comprises at least one acceleration sensor configured for generating acceleration sensor data, wherein the at least one wearable sensor patch is further configured for transmitting the acceleration sensor data to at least one evaluation unit by using a connection interface; b. the at least one evaluation unit according to any one of the preceding Embodiments referring to an evaluation unit, wherein the at least one evaluation unit is further configured for receiving the acceleration sensor data transmitted by the wearable sensor patch by using a connection interface.Embodiment 33 : The medical system according to the preceding Embodiment, the medical system further comprisingc. at least one remote device, wherein the remote device comprises the evaluation unit, wherein the remote device is physically separate from the wearable sensor patch.Short description of the FiguresFurther optional features and embodiments will be disclosed in more detail in the subsequent description of embodiments, preferably in conjunction with the dependent claims. Therein, the respective optional features may be realized in an isolated fashion as well as in any arbitrary feasible combination, as the skilled person will realize. The scope of the invention is not restricted by the preferred embodiments. The embodiments are schematically depicted in the Figures. Therein, identical reference numbers in these Figures refer to identical or functionally comparable elements.In the Figures:Figure 1 shows an exemplary computer-implemented method for determining a position of a wearable sensor patch in a horizontal direction;Figure 2 shows a plurality of axes and planes defined by a human body; andFigure 3 shows an exemplary medical system comprising an exemplary wearable sensor patch and an exemplary remote device comprising an evaluation unit; andFigure 4 shows the principle of determining the position of a wearable sensor patch in the horizontal direction.Detailed description of the embodimentsAs may be derived from Figure 1, first, an orientation of the wearable sensor patch 182 relative to a world coordinate system may be determined. For doing so, the wearable sensor patch 182 may be assumed to be place on the right side of the chest of the user 196 at roughly an predetermined right place. At least one failsafe may be implemented to detect a severe misplacement of the wearable sensor patch 182, such as a placement on the back of the user 196.Furthermore, an automatic orientation detection may require evaluating data related to a forward or backward movement of the user 196. To identify the forward or backward movement of the user 196 without knowing the patch orientation, an orientation invariant feature, such as e.g. an orientation invariant summed variance feature may be used. The interplay of known gravitational acceleration and forward movement and / or backward movement may allow the determination of all sensor axis orientations in the world coordinate system.When the orientation of the wearable sensor patch 182 is known, the position of the sensor patch in a horizontal direction may be determined by tracking the normal / daily motion activities for a sufficient amount of time until a plurality of left turns and right turns have been recorded. A comparison of an rotation of the left turns and the right turns may results in identifying whether the patch is located centered, left- or right-shifted relative to a horizontal center.In Figure 1, an exemplary computer-implemented method 110 for determining a position of a wearable sensor patch 182 in a horizontal direction, wherein the wearable sensor patch 182 is positioned on a user 196’s chest, wherein the wearable sensor patch 182 comprises at least one acceleration sensor 184 configured for generating acceleration sensor 184 data is shown.The method 110 may comprise:- generating acceleration sensor 184 data (denoted by reference number 158) by using the wearable sensor patch 182 while the wearable sensor patch 182 is positioned on a user 196’s chestThe method 110 may comprise:- determining whether the recorded acceleration sensor 184 data comprises sufficient data on at least one left turn of the user 196 and at least one right turn of the user 196 or not (denoted by reference number 160).The method 110 comprises:- receiving the acceleration sensor data (denoted by reference number 112), wherein the acceleration sensor data is related to a movement of the user 196,- determining at least one left turn of the user 196 and at least one right turn of the user 196 (denoted by reference number 114), preferably while the user 196 is moving in a forward or a backward direction, by evaluating the acceleration sensor data and, thereby, generating turn data, and- determining a horizontal position of the wearable sensor patch 182 on the user 196’s chest (denoted by reference number 116) by evaluating the turn data, preferably wherein the horizontal position is determined relative to a horizontal center of the user 196’s chest.The received acceleration sensor data may comprise data related to three acceleration vectors, wherein each acceleration vector is parallel to a different acceleration axis; wherein each acceleration axis is orthogonal to any one of the further acceleration axis.Determining the at least one left turn and the at least one right turn (denoted by reference number 114) may comprise o identifying the at least one left turn (denoted by reference number 118) within the acceleration sensor data by comparing the acceleration sensor data to at least one known acceleration pattern related to at least one left turn; o identifying the at least one right turn (denoted by reference number 120) within the acceleration sensor data by comparing the acceleration sensor data to at least one known acceleration pattern related to at least one right turn.Determining the horizontal position of the wearable sensor patch 182 on the user 196’s chest (denoted by reference number 116) by evaluating the turn data may comprise o comparing the acceleration sensor data (denoted by reference number 122) related to the at least one left turn and the acceleration sensor data related to the at least one right turn to each other.Comparing the acceleration sensor data (denoted by reference number 122) related to the at least one left turn and the acceleration sensor data related to the at least one right turn to each other may comprise o determining at least one rotation for the at least one left turn (denoted by reference number 124) by evaluating the turn data; o determining at least one rotation for the at least one right turn (denoted by reference number 126) by evaluating the turn data.Comparing the acceleration sensor data (denoted by reference number 122) related to the at least one left turn and the acceleration sensor data related to the at least one right turn may comprise o comparing the determined at least one rotation (denoted by reference number 128) for the at least one left turn and the determined at least one rotation for theat least one right turn to each other in order to determine the horizontal position of the wearable sensor patch 182 on the user 196’s chest, preferably wherein the horizontal position is determined relative to the horizontal center of the user 196’s chest.Comparing the determined at least one rotation (denoted by reference number 128) for the at least one left turn and the determined at least one rotation for the at least one right turn to each other may comprise o determining a turning angle (denoted by reference number 130) of the at least one left turn and a turning angle of the at least one right turn, wherein the turning angle is the angle between an initial direction of movement before making the respective turn and a final direction of movement after making the respective turn.The at least one rotation for the at least one left turn and the at least one rotation for the at least one right turn that are compared to each may be selected to have a difference between the absolute value of the determined turning angle of the at least one left turn and the absolute value of the determined turning angle of the at least one right turn below a threshold.The respective turning angle may be determined by using a gyroscope 186, wherein, for using the gyroscope 186, gyroscope sensor data generated by the gyroscope 186 is evaluated in order to determine the respective turning angle.Comparing the determined at least one rotation for the at least one left turn and the determined at least one rotation for the at least one right turn to each other may comprise o determining a turn length (denoted by reference number 132) of the at least one left turn and a turn length of the at least one right turn, wherein the turn length is a distance that the user traveled while making the respective turn.The at least one rotation for the at least one left turn and the at least one rotation for the at least one right turn that are compared to each other may be selected to have a difference between the absolute value of the determined turn length of the at least one left turn and the absolute value of the determined turn length of the at least one right turn below a threshold.The respective turn length may be determined by using at least one of: o a GPS sensor 188, wherein, for using the GPS sensor 188, GPS sensor data generated by the GPS sensor 188 is evaluated in order to determine the respective turn length;o the acceleration sensor 184, wherein, for using the acceleration sensor 184, the acceleration sensor data is evaluated in order to determine the respective turn length; o an Ultra Wideband module 206, wherein, for using the Ultra Wideband module 206, Ultra Wideband module data generated by the Ultra Wideband module 206 is evaluated in order to determine the respective turn length.Comparing the determined at least one rotation for the at least one left turn and the determined at least one rotation for the at least one right turn to each other may comprise o determining round trip data (denoted by reference number 134) on a round trip of the user 196 comprising an outward path and a return path, wherein the outward path and the return path at least partially overlap and, thereby, define an at least partial overlap, o determining at least one pair of recurrent turns (denoted by reference number 136) on the at least partial overlap of the outward path and the return path, wherein the at least one pair of recurrent turns comprises a specific left turn and a right turn, wherein the specific left turn and the specific right turn correspond to the same turn on the at least partial overlap.The at least one rotation for the at least one left turn and the at least one rotation for the at least one right turn that are compared to each other may be selected to be the specific left turn and the specific right turn comprised by the at least one pair of recurrent turns.The round trip data may be determined by using at least one of: o the acceleration sensor 184, wherein, for using the acceleration sensor 184, the acceleration sensor data generated by the acceleration sensor 184 is evaluated in order to find at least one recurring pattern in the acceleration sensor data indicating a round trip; o the GPS sensor 188, wherein, for using the GPS sensor 188, GPS sensor data generated by the GPS sensor 188 is evaluated; o the gyroscope 186, wherein, for using the gyroscope 186, gyroscope sensor data generated by the gyroscope 186 is evaluated in order to determine the respective turning angle wherein, for using the movement pattern, the movement pattern is evaluated in order to determine the respective turning angle of a specific turn in the outward path and the respective turning angle of the specific turn in the return pathThe horizontal position of the wearable sensor patch 182 on the user 196’s chest may be determined by using at least one algorithm configured to determine the horizontal position of the wearable sensor patch 182 on the user 196’s chest, wherein the algorithm configured to determine the horizontal position of the wearable sensor patch 182 on the user 196’s chest may comprise at least one of:■ a look-up table;■ a machine learning model;■ an analytical model.The method 110 further may comprise:- determining a gyrocardiogram of the user 196 (denoted by reference number 138) by evaluating the acceleration sensor data and the horizontal position of the wearable sensor patch 182 on the user 196’s chest.The gyrocardiogram of the user 196 may be determined by using at least one algorithm configured to determine the gyrocardiogram of the user 196, wherein the algorithm configured to determine the gyrocardiogram of the user 196 may comprise at least one of:■ a look-up table;■ a machine learning model;■ an analytical model.Determining a gyrocardiogram of the user 196 (denoted by reference number 138) by evaluating the acceleration sensor data and the horizontal position of the wearable sensor patch 182 on the user 196’s chest may comprise o compensating an influence of the determined horizontal position (denoted by reference number 140) of the wearable sensor patch 182 on the user 196’s chest on the gyrocardiogram of the user 196.Compensating an influence of the determined horizontal position (denoted by reference number 140) of the wearable sensor patch 182 on the user 196’s chest on the acceleration sensor data may comprise at least one of: o correcting the acceleration sensor data; o selecting a specific algorithm configured to determine the gyrocardiogram of the user 196.The method 110 may comprise:- transferring the acceleration sensor data (denoted by reference number 142) generated in a known local coordinate system of the acceleration sensor 184 into a world coordinate system by evaluating a transformation matrix, preferably wherein data related to or based on the transferred acceleration sensor data is evaluated when determining the gyrocardiogram of the user 196. *** Bitte nochmal priifen. Danke! ***The transformation matrix may be determined by o assuming an initial pose of the wearable sensor patch 182 (denoted by reference number 156).In the initial position it may be assumed that a vertical axis of the wearable sensor patch points in a horizontal direction, This may be possible, due to the attachment of the wearable sensor patch to the chest of the user. Further assumptions on the remaining two axes may also be possible, because there is may be at least one marking on the wearable sensor patch indicating a correct placement.The acceleration sensor data may be further related to a gravitational acceleration. The transformation matrix may be determined by o determining a first axis of the world coordinate system (denoted by reference number 144) by evaluating at least one gravitational acceleration vector within the acceleration sensor data, wherein the first axis is aligned parallel to the gravitational acceleration vector, preferably wherein the first axis is aligned in a manner that said first axis is pointing into a direction of the gravitational acceleration vector.Determining the first axis of the world coordinate system (denoted by reference number 144) may comprise o identifying the at least one gravitational acceleration vector (denoted by reference number 146) within the acceleration sensor data by comparing the acceleration sensor data to at least one known absolute value of the gravitational acceleration.The transformation matrix may be determined by o determining a second axis of the world coordinate system vector (denoted by reference number 148) by evaluating a straight forward movement vector within the acceleration sensor data, wherein the second axis is aligned parallel to the straight forward movement vector, preferably wherein the second axis is alignedin a manner that said second axis is pointing into a direction of the straight forward movement vector.Determining a second axis of the world coordinate system (denoted by reference number 148) may comprise o identifying the at least one straight forward movement vector (denoted by reference number 150) within the acceleration sensor data by comparing the acceleration sensor data to at least one known movement pattern related to at least one straight forward movement vector, preferably and by taking the first axis of the world coordinate system into account.The transformation matrix may be determined by o determining a third axis of the world coordinate system (denoted by reference number 152) by evaluating the second axis and the first axis, wherein the third axis of the world coordinate system is aligned orthogonal to the second axis, wherein the third axis of the world coordinate system is aligned orthogonal to the first axis, wherein, by determining the third axis of the world coordinate system, the known world coordinate system is derived.The transformation matrix may be determined by o determining the transformation matrix (denoted by reference number 154) by evaluating the known local coordinate system and the known world coordinate system.As already indicated a horizontal position may be determined. A horizontal position may refer to a specific position on a horizontal axis 162. Said horizontal axis may be defined by the human 194 body, as may be derived from Figure 2. Said horizontal axis may, alternatively, be referred to as frontal axis. The horizontal axis may be defined by an intersection of a frontal plane 164 (also referred to as coronal plane) and a transverse plane 166 that are further defined by the human 194 body. The horizontal axis may be parallel to a ground and / or a floor when a human 194 is standing in an anatomical position. In addition, the horizontal axis may run from the left side of the human 194 to the right side of the human 194. In the anatomical position the human 194 is standing upright, the human 194 is facing forward, the human 194 has the arms at the sides and the human 194 has the palms facing forward.For the horizontal position being determined relative to a horizontal center of the user 196’s chest, the horizontal center of the human 194 chest may define a reference point for the determined relative position. In order to do so, the horizontal center may define the origin of the horizontal axis. The horizontal center may be a position of a center of a human 194 chest on the horizontal axis. The center of the human 194 chest on the horizontal axis may be on a sagittal plane 168 that is defined by the human 194 body. The human 194 body may further define a sagittal axis 170 and / or a vertical axis 172 (also referred to as longitudinal axis).In a Figure 3, an exemplary evaluation unit 174 for determining a position of a wearable sensor patch 182 in a horizontal direction is shown, wherein the evaluation unit 174 is configured for performing the computer-implemented method 110 for determining a position of a wearable sensor patch 182 in a horizontal direction as elsewhere disclosed herein.The evaluation unit 174, for being configured for performing the computer-implemented method 110 as elsewhere described herein, may comprise a processor 176 and a memory 178 with instructions, which when executed cause the processor to perform the computer-implemented method 110 as elsewhere disclosed herein.The evaluation unit 174 may be comprised by at least one remote device 180, wherein the remote device 180 is physically separate from the wearable sensor patch 182.Further in Figure 3, an exemplary is shown. The remote device 180 comprises at least one evaluation unit 174 as elsewhere described herein, wherein the remote device 180 is physically separate from the wearable sensor patch 182.The remote device 180 may comprise a data communication device 190, preferably for receiving acceleration sensor data. The wearable sensor patch 182 may comprise a data communication device 192, preferably for transmitting acceleration sensor data.The term "data communication device " as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an arbitrary device configured for transmitting and / or receiving data, such as a wireless or wire-bound interface for data communication data.Further in Figure 3, the wearable sensor patch 182 is shown. The wearable sensor patch 182 comprises the acceleration sensor 184. The wearable sensor patch 182 may comprise at leastone of: a gyroscope 186, a GPS sensor 188. Further, an orientation may be determined by using an Inertial Measurement Unit 204. The Inertial Measurement Unit 204 may be comprised by the wearable sensor patch 182. The wearable sensor patch 182 may comprise the Ultra Wideband module 206.Further in Figure 3, an exemplary medical system is shown, the medical system comprises a. at least one wearable sensor patch 182 configured for being positioned on a user 196’s chest, wherein the wearable sensor patch 182 comprises at least one acceleration sensor 184 configured for generating acceleration sensor data, wherein the at least one wearable sensor patch 182 is further configured for transmitting the acceleration sensor data to at least one evaluation unit 174 by using a connection interface; b. the at least one evaluation unit 174 as elsewhere disclosed herein referring to an evaluation unit 174, wherein the at least one evaluation unit 174 is further configured for receiving the acceleration sensor data transmitted by the wearable sensor patch 182 by using a connection interface.The medical system further may comprise c. at least one remote device 180, wherein the remote device 180 comprises the evaluation unit 174, wherein the remote device 180 is physically separate from the wearable sensor patch 182.The principle on how to determine the horizontal position of the wearable sensor patch 182 on the user 196’s chest may be derived from Figure 4. Figure 4 shows a top view onto the user 196. In Figure 4 a user 196 performing a left turn (left side of Figure 4) and a right turn (right side of Figure 4) is shown. The turns are indicated by the arrows. The wearable sensor patch 182 depicted in three differing horizontal positions on the chest of the user 196. In the first horizontal position 198, the wearable sensor patch 182 is on the left side of the chest of the user 196. In the second horizontal position 200, the wearable sensor patch 182 is in the center of the chest of the user 196. In the third horizontal position 202, the wearable sensor patch 182 is on the right side of the chest of the user 196. As may be derived from the Figure 4, the rotation depends differs when the horizontal position of the wearable sensor patch 182 is different.In a further aspect, a computer program comprising instructions is disclosed (not shown) which, when the program is executed by the evaluation as elsewhere disclosed herein, causethe evaluation unit 174 to perform the computer-implemented method 110 as elsewhere disclosed herein.In a further aspect, a computer-readable storage medium comprising instructions is disclosed (not shown) which, when the instructions are executed by the evaluation unit 174 as elsewhere disclosed herein cause the evaluation unit 174 to perform the computer-implemented method 110 as elsewhere disclosed herein.In a further aspect, a non-transient computer-readable medium comprising instructions is disclosed (not shown) which, when the instructions are executed by the evaluation as elsewhere disclosed herein cause the evaluation unit 174 to perform the computer-implemented method 110 as elsewhere disclosed herein.List of reference numbers computer-implemented method for determining a position of a wearable sensor patch in a horizontal direction receiving the acceleration sensor data determining at least one left turn of the user and at least one right turn of the user determining a horizontal position of the wearable sensor patch on the user’s chest identifying the at least one left turn identifying the at least one right turn comparing the acceleration sensor data determining at least one rotation for the at least one left turn determining at least one rotation for the at least one right turn comparing the at least one rotation determining a turning angle determining a turn length determining round trip data determining at least one pair of recurrent turns determining a gyrocardiogram of the user compensating an influence of the determined horizontal position coordinate-transferring the acceleration sensor data determining a first axis of the world coordinate system identifying the at least one gravitational acceleration vector determining a second axis of the world coordinate system vector identifying the at least one straight forward movement vector determining a third axis of the world coordinate system determining the transformation matrix assuming an initial pose of the wearable sensor patch generating acceleration sensor data determining whether the recorded acceleration sensor data comprises sufficient data on at least one left turn of the user and at least one right turn of the user or not horizontal axis frontal plane transverse plane sagittal planesagittal axis vertical axis evaluation unit processor memory remote device wearable sensor patch acceleration sensor gyroscopeGPS sensor data communication device data communication device human user first horizontal position second horizontal position third horizontal position Inertial Measurement Unit Ultra Wideband module

Claims

Claims1. A computer-implemented method for determining a position of a wearable sensor patch (182) in a horizontal direction, wherein the wearable sensor patch (182) is positioned on a user’s chest, wherein the wearable sensor patch (182) comprises at least one acceleration sensor (182) configured for generating acceleration sensor (182) data, the method comprising:- receiving the acceleration sensor data, wherein the acceleration sensor data is related to a walking movement of the user (196),- wherein the acceleration sensor data comprises data related to the user walking in a forward or a backward direction during at least one left turn of the user and at least one right turn of the user,- determining at least one left turn of the user (196) and at least one right turn of the user (196), by evaluating the acceleration sensor data and, thereby, generating turn data, and- determining a horizontal position of the wearable sensor patch (182) on the user’s chest by evaluating the turn data, preferably wherein the horizontal position is determined relative to a horizontal center of the user’s chest- wherein comparing the acceleration sensor data related to the at least one left turn and the acceleration sensor data related to the at least one right turn to each other is performed in order to determine an asymmetry in the turn data that is introduced due to a positioning of the wearable sensor patch in a horizontal direction that does not coincide with the horizontal center of the user’s chest.

2. The method according to the preceding claim, wherein determining the horizontal position of the wearable sensor patch (182) on the user’ s chest by evaluating the turn data comprises o comparing the acceleration sensor data related to the at least one left turn and the acceleration sensor data related to the at least one right turn to each other.

3. The method according to the preceding claim, wherein comparing the acceleration sensor data related to the at least one left turn and the acceleration sensor data related to the at least one right turn to each other comprises o determining at least one rotation for the at least one left turn by evaluating the turn data; o determining at least one rotation for the at least one right turn by evaluating the turn data.

4. The method according to the preceding claim, wherein comparing the acceleration sensor data related to the at least one left turn and the acceleration sensor data related to the at least one right turn comprises o comparing the determined at least one rotation for the at least one left turn and the determined at least one rotation for the at least one right turn to each other in order to determine the horizontal position of the wearable sensor patch (182) on the user’s chest.

5. The method according to the preceding claim, wherein comparing the determined at least one rotation for the at least one left turn and the determined at least one rotation for the at least one right turn to each other comprises o determining a turning angle of the at least one left turn and a turning angle of the at least one right turn, wherein the turning angle is the angle between an initial direction of movement before making the respective turn and a final direction of movement after making the respective turn; wherein the at least one rotation for the at least one left turn and the at least one rotation for the at least one right turn that are compared to each other are selected to have a difference between the absolute value of the determined turning angle of the at least one left turn and the absolute value of the determined turning angle of the at least one right turn below a threshold.

6. The method according to any one of the two preceding claims, wherein comparing the determined at least one rotation for the at least one left turn and the determined at least one rotation for the at least one right turn to each other comprises o determining a turn length of the at least one left turn and a turn length of the at least one right turn, wherein the turn length is a distance that the user traveled while making the respective turn; wherein the at least one rotation for the at least one left turn and the at least one rotation for the at least one right turn that are compared to each other are selected to have adifference between the absolute value of the determined turn length of the at least one left turn and the absolute value of the determined turn length of the at least one right turn below a threshold.

7. The method according to any one of the three preceding claims, wherein comparing the determined at least one rotation for the at least one left turn and the determined at least one rotation for the at least one right turn to each other comprises o determining round trip data on a round trip of the user (196) comprising an outward path and a return path, wherein the outward path and the return path at least partially overlap and, thereby, define an at least partial overlap, o determining at least one pair of recurrent turns on the at least partial overlap of the outward path and the return path, wherein the at least one pair of recurrent turns comprises a specific left turn and a right turn, wherein the specific left turn and the specific right turn correspond to the same turn on the at least partial overlap, wherein the at least one rotation for the at least one left turn and the at least one rotation for the at least one right turn that are compared to each other are the specific left turn and the specific right turn comprised by the at least one pair of recurrent turns.

8. The method according to any one of the preceding claims, the method further comprising:- determining a gyrocardiogram of the user (196) by evaluating the acceleration sensor data and the horizontal position of the wearable sensor patch (182) on the user’s chest.

9. The method according to the preceding claim, wherein determining a gyrocardiogram of the user (196) by evaluating the acceleration sensor data and the horizontal position of the wearable sensor patch (182) on the user’s chest comprises o compensating an influence of the determined horizontal position of the wearable sensor patch (182) on the user’s chest on the gyrocardiogram of the user (196).

10. The method according to any one of the preceding claims, the method comprising:- transferring the acceleration sensor data generated in a known local coordinate system of the acceleration sensor (182) into a world coordinate system by evaluating a transformation matrix.

11. An evaluation unit (174) for determining a position of a wearable sensor patch (182) in a horizontal direction, wherein the evaluation unit (174) is configured for performing the computer-implemented method for determining a position of a wearable sensor patch (182) in a horizontal direction according to any one of the preceding method claims.

12. A remote device (180), wherein the remote device (180) comprises at least one evaluation unit (174) according to any one of the preceding claims referring to an evaluation unit (174), wherein the remote device (180) is physically separate from the wearable sensor patch (182).

13. A computer program comprising instructions which, when the program is executed by the evaluation unit (174) according to any one of the preceding claims referring to an evaluation unit (174), cause the evaluation unit (174) to perform the computer-implemented method according to any one of the preceding method claims.

14. A, preferably non-transient, computer-readable storage medium comprising instructions which, when the instructions are executed by the evaluation unit (174) according to any one of the preceding claims referring to an evaluation unit (174) cause the evaluation unit (174) to perform the computer-implemented method according to any one of the preceding method claims.

15. A medical system comprising a. at least one wearable sensor patch (182) configured for being positioned on a user’s chest, wherein the wearable sensor patch (182) comprises at least one acceleration sensor (182) configured for generating acceleration sensor data, wherein the at least one wearable sensor patch (182) is further configured for transmitting the acceleration sensor data to at least one evaluation unit (174) by using a connection interface; b. the at least one evaluation unit (174) according to any one of the preceding claims referring to an evaluation unit (174), wherein the at least one evaluation unit (174) is further configured for receiving the acceleration sensor data transmitted by the wearable sensor patch (182) by using a connection interface.

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