Method and technical device for determining at least one quality, in particular smart device, in particular smart watch

Abstract

The invention relates to a method for determining at least one mass (m) lifted and / or carried by a user (B) by means of a technical device (100), preferably in the form of a smartwatch and / or a sports wristband, having at least one sensor (10), which is configured to measure at least one dynamic motion parameter, wherein a sensor signal of at least one sensor (10) is processed and / or evaluated in order to determine at least one mass (m) lifted and / or carried by a user (B).

Description

[0001] This invention relates to a method and technical apparatus, particularly a smart device, preferably in the form of a smartwatch and / or sports wristband, for a user to determine at least one mass they lift and / or carry. Furthermore, this invention relates to the corresponding use of such a technical device, particularly a smart device, to determine at least one mass lifted and / or carried by the user of the device. Specifically, this invention relates to a corresponding method for determining at least one mass, particularly the mass lifted and / or carried by a user, using such a technical device, particularly a smart device. Furthermore, this invention relates to: a corresponding computer program product for executing the corresponding method; a corresponding storage medium on which the corresponding computer program product is stored; and a corresponding data carrier signal for transmitting the corresponding computer program product. Furthermore, this invention relates to a corresponding computing unit for executing the corresponding method, wherein, in particular, the computing unit can be used in such a technical device, particularly a smart device.

[0002] For example, many workers in moving companies, handicraft businesses, and / or the nursing industry face situations where they lift and carry heavy objects using their own muscle strength without mechanical assistance. Besides work activities involving lifting and carrying heavy objects, heavy objects are also frequently moved unconsciously in daily life. Lifting heavy objects can quickly lead to significant lumbar flexion. Frequent bending and stretching of the lumbar spine under load can lead to herniated discs and back pain.

[0003] Determining mass is no easy task, especially without specialized weighing instruments. Mass determination often exceeds the capabilities of human intuition, and like brightness or volume, it can only be estimated with difficulty and inaccuracy.

[0004] However, such problems frequently arise in daily life, especially when it comes to preventing excessive weight-bearing: How heavy was the hiking backpack I carried all weekend? - In the long run, how many cases of beer are appropriate for my back and health? Will I still be able to carry my grandchildren? Basically, there are known weighing scales such as luggage scales or baby scales. These types of scales are often not always convenient, especially for home and everyday use.

[0005] Therefore, the object of the present invention is to at least partially overcome at least one of the aforementioned disadvantages. In particular, the object of the present invention is to provide a method and technical device, especially a smart device, preferably in the form of a smartwatch and / or sports wristband, for a user to determine at least one mass that they can lift and / or carry. Preferably, the object of the present invention is to allow for easy and rapid determination of mass, preferably without any additional instruments (e.g., dedicated weighing scales), but preferably by means of existing resources that the user can carry with them, especially in the home and in daily use, such as technical devices, especially smart devices. Preferably, the object of the present invention is to provide comprehensive practicality and enhanced functionality in the corresponding technical device, especially a smart device. In particular, the object of the present invention is to provide a universal method for determining at least one mass lifted and / or carried by a user of a technical device, especially a smart device. Furthermore, the object of the present invention is to provide: a corresponding computer program product for performing the corresponding method; a corresponding storage medium on which the corresponding computer program product is stored; and a corresponding data carrier signal for transmitting the corresponding computer program product. Additionally, the object of the present invention is to provide a corresponding computing unit for performing the corresponding method, wherein, in particular, the computing unit can be used in the corresponding technical device, especially a smart device.

[0006] The aforementioned task is accomplished by a method and technical device, particularly a smart device, having the features of independent method and device claims, for allowing a user to determine at least one mass that they lift and / or carry, preferably in the form of a smartwatch and / or a sports wristband. Furthermore, the aforementioned task is accomplished by a corresponding use of the corresponding technical device, particularly a smart device, to determine at least one mass that a user of the technical device lifts and / or carries and / or to measure liquid intake. Furthermore, the aforementioned task is accomplished by a corresponding method, having the features of independent method claims, for using the corresponding technical device, particularly a smart device, to determine at least one mass that can be lifted and / or carried by a user. Furthermore, the aforementioned task is accomplished by a corresponding computer program product for performing the corresponding method, a corresponding storage medium storing the corresponding computer program product, and a corresponding data carrier signal for transmitting the corresponding computer program product. Furthermore, the aforementioned task is accomplished by a corresponding computing unit, having the features of dependent device claims, for performing the corresponding method, wherein, in particular, the computing unit can be used in the corresponding technical device.

[0007] According to one aspect, the present invention provides a technical device, particularly a smart device, preferably in the form of a smartwatch and / or wristband, for allowing a user to determine at least one mass that can be lifted and / or carried by them. include: - At least one sensor, particularly a motion sensor, configured to measure at least one dynamic motion parameter, and - Computing unit, The computing unit is specifically configured to process (and / or analyze) sensor signals from at least one sensor, and to determine at least one mass that the user lifts and / or carries based on the processing (and / or analysis).

[0008] In the context of this disclosure, technical devices, especially smart devices, can refer to any smart device that a user can carry, such as mobile phones, smartwatches, wristbands, sports wristbands, heart rate monitors, ankle bracelets, smart clothing, rings, gloves, camera equipment, smart glasses, headphones, etc., that have certain logical functions and sensing devices.

[0009] Within the scope of this disclosure, a sensor, particularly a motion sensor, can refer to any sensor capable of measuring dynamic motion parameters such as position, velocity, and / or acceleration. A sensor within the scope of this disclosure can refer to any sensor capable of measuring position, motion, acceleration, tilt, force, rotation, angular momentum, field parameters (e.g., magnetic fields), gravitational parameters, or similar parameters. Sensors within the scope of this disclosure can be electromechanical sensors and / or electromagnetic sensors. Sensors within the scope of this disclosure can sense dynamic motion parameters in at least two or three directions, or can be configured as 2D or 3D sensors.

[0010] In the context of this disclosure, mass can refer to an object having a certain weight. In the context of this disclosure, mass can refer to load or weight.

[0011] The proposed technology allows for easy and rapid quality determination. No additional equipment is required; a smartwatch can suffice.

[0012] Therefore, the concept is as follows: When a mass is moved (e.g., lifted by hand or "weighed" in the hand, i.e., moved up and down), the motion dynamics and / or motion type are collected and analyzed. Such characteristic motions can be modeled and learned by artificial intelligence. These characteristic motions can be modeled for at least one mass or for different masses. Such characteristic motions can be subject to user-specific analysis, for example, by calibrating and / or normalizing sensor signals. Calibration can be performed in a user-specific manner. For this purpose, a training run (also known as a calibration run) can be performed, for example, using technical equipment.

[0013] A significant advantage of this invention is the universal availability of such technological devices, as they may always be with you or readily available, for example, when a smartwatch is worn on your wrist.

[0014] Furthermore, such technological devices offer combined advantages and enhanced functionality. Advantageously, these devices can be further developed into assistants for analyzing individual loads when lifting and carrying heavy objects.

[0015] Furthermore, such technological devices can record and accumulate unconscious lifting and carrying activities throughout the day, sending warning messages if the load becomes too high, for example, with the aid of the same technological device or other mobile devices the user has. In this way, users can increase their awareness of lifting and carrying heavy objects in daily life and, when necessary, use their actions to prevent conditions such as herniated discs and back pain.

[0016] In addition, such technological devices can be used to create stress curves and transmit them to healthcare providers, such as employers and / or physicians.

[0017] Furthermore, such technological devices enable beneficial interaction with the user. The user can be informed of a defined quality and / or corresponding load curve. The user can be instructed to calibrate sensor signals and / or record different signal examples and / or frequency examples, for example, for one quality or multiple different qualities. For this purpose, the technological device can be designed with a user interface.

[0018] Furthermore, it is conceivable that such technological devices could be personalized for specific users, for example, as personal smartwatches. Additionally, it is conceivable that such devices could be used as general-purpose measuring instruments, for example, in gyms or doctors' offices.

[0019] Furthermore, it can be specified that at least one sensor includes at least one acceleration sensor, at least one force sensor, at least one tilt sensor, at least one magnetic field sensor, at least one gravity sensor, and / or at least one combination of sensors. With the aid of such sensors, motion dynamics can be sensed when the mass is moving. Motion can be easily analyzed using laws of motion, thus allowing the analysis to be performed with less computational effort. One possibility is to analyze the motion of the mass under the influence of a measured force (such as a spring force). The acceleration of an object is directly proportional to the force acting on it and inversely proportional to its mass. If the sensor can detect the force acting on the object and / or the resulting acceleration, then the mass can be determined as force divided by acceleration. Another possibility is to analyze the motion of the mass using a measured velocity. If the velocity of the object is known, its momentum can also be determined, which is proportional to both mass and velocity. If the sensor can measure the velocity and / or the resulting momentum, then the mass can be determined as momentum divided by velocity.

[0020] Furthermore, it can be specified that at least one sensor may have at least one electromechanical sensor, particularly a piezoelectric sensor and / or a capacitive sensor, and at least one electromagnetic sensor, particularly a rotational acceleration sensor and / or an inductive sensor. Such sensors enable simple and low-cost motion detection.

[0021] Furthermore, it can be specified that at least one sensor may include at least one 3D sensor. This enables improved motion dynamics analysis, which can be performed in different directions.

[0022] Furthermore, a storage unit may be provided, wherein the storage unit has a permanent storage area. Preferably, the permanent storage area can be used to store code that can be used to execute corresponding methods for determining quality. Further, the permanent storage area can be used to store feature maps and / or artificial neural networks, thereby enabling simple and efficient analysis of detected motion dynamics and / or motion types.

[0023] In addition, a storage unit may be provided, wherein the storage unit may have a volatile storage area. Preferably, the volatile storage area can be used as the working memory of the computing unit.

[0024] Furthermore, a storage unit can be provided, wherein the storage unit has at least one first storage area, particularly permanent, wherein a feature map is stored, which has different signal examples and / or frequency examples of sensor signals (particularly calibrated and / or normalized) from at least one sensor for a corresponding quality. Furthermore, it is conceivable that the different signal examples and / or frequency examples of sensor signals (particularly calibrated and / or normalized) from at least one sensor for a corresponding quality can be stored for at least one user or for multiple different users. Furthermore, it is conceivable that the different signal examples and / or frequency examples of sensor signals (particularly calibrated and / or normalized) from at least one sensor can be stored for different motion types, e.g., for one type of motion (lifting) and / or another type of motion (carrying while walking), etc. With the help of the feature map, rapid analysis of motion dynamics and / or motion types can be achieved with less computational effort. In this process, the computing unit can search for patterns in new sensor signals that are comparable to the stored signal examples or frequency examples to determine the corresponding quality.

[0025] Furthermore, a storage unit can be provided, wherein the storage unit has at least one second storage area, particularly permanent, in which an artificial neural network (preferably self-learning) is stored, which has been specifically trained and / or can be trained to determine the corresponding quality based on sensor signals from at least one sensor (particularly calibrated and / or normalized). In this way, particularly fast and accurate analysis of motion dynamics (e.g., velocity) and / or motion types (e.g., lifting versus carrying) can be achieved. The artificial neural network can be advantageously used for signal processing, particularly for performing the analysis of signal curves and / or signal frequencies. The artificial neural network can be trained to identify patterns in sensor signals, including frequency patterns. For example, the artificial neural network can be trained on a dataset of signal data having known signal examples and / or frequency examples, and subsequently used to identify and classify similar signal examples and / or frequency examples in new signals. Advantageously, the artificial neural network can perform Fourier transforms and wavelet transforms, which can be advantageously used to analyze the frequency components of signals. These techniques are widely used in signal processing to extract information from signals in different domains (e.g., time domain, frequency domain, and time-frequency domain).

[0026] Furthermore, it can be specified that the computing unit has an electronic unit specifically configured to process sensor signals from at least one sensor, particularly calibrated and / or normalized sensor signals, using frequency analysis methods. In this way, sensor signals can be easily and quickly analyzed using established signal processing methods.

[0027] Advantageously, the computing unit can be specifically configured to analyze the frequency, amplitude, and / or shape of a sensor signal (especially after calibration and / or normalization) from at least one sensor. Motion dynamics and / or motion type can be described using the frequency, amplitude, and / or shape of the sensor signal (especially after calibration and / or normalization). Motion dynamics and / or motion type can be specific to the mass of the moving object. The moving mass can be easily and quickly determined based on the frequency, amplitude, and / or shape (especially transients and / or slope) of the sensor signal (especially after calibration and / or normalization) from at least one sensor. For example, it is conceivable that if a user is carrying a mass while walking, a relatively heavier mass may exhibit a higher amplitude in the sensor signal compared to a lighter mass. Furthermore, it is conceivable that when a user is carrying a mass while walking, a relatively heavier mass may exhibit a lower frequency in the sensor signal compared to a lighter mass. A specific proportional relationship may also appear in the sensor signal when the user lifts a mass. A fairly flat transient in the sensor signal may indicate that a relatively heavy mass is being lifted. A fairly steep transient in the sensor signal may be an indication that a relatively light mass is being lifted, and so on.

[0028] Furthermore, it can be specified that the computing unit has at least one filter, particularly a high-pass filter and / or a low-pass filter, to process sensor signals from at least one sensor, particularly calibrated and / or normalized sensor signals, for determining whether a user is carrying and / or lifting a mass. Thresholds for different signal frequencies can be easily and quickly set using the filters. In this way, excessively high and / or low frequencies in the sensor signal can be easily and quickly detected.

[0029] On the one hand, it is advantageous that the computing unit can be specifically configured to process (or analyze) different signal examples and / or frequency examples of sensor signals (especially calibrated and / or normalized) of at least one sensor using characteristic curve plots to determine quality.

[0030] On the other hand, it is advantageous that the computing unit can be specifically configured to process (or analyze) sensor signals from the sensor, especially calibrated and / or normalized sensor signals, using artificial neural networks to determine quality.

[0031] Furthermore, the computing unit can be specifically configured to calibrate and / or normalize sensor signals from at least one sensor based on at least one vital parameter of the user, said vital parameter specifically including: weight, height, body density, BMI, pulse, heart rate, blood volume, blood pressure, blood oxygen saturation, and / or body temperature. In this way, user-specific sensor signal analysis can be achieved. Users with different physiques and / or levels of training may lift and / or carry comparable masses in different ways. For example, a trained furniture mover may carry heavy objects more easily than a user who does not frequently lift and / or carry heavy objects due to occupational reasons (e.g., office work). However, the same user's immediate state (e.g., after rest or after a long workday) may also affect sensor signals. Such user-specific factors can be advantageously mapped using vital parameters and considered in signal analysis so that masses can be determined equally accurately for different users and / or for the same user in different situations. For example, different vital parameters of the user can be provided as input to a corresponding neural network. Furthermore, considering the user's vital parameters in the context of feature maps is also conceivable.

[0032] Furthermore, the computing unit can be specifically configured to process and / or analyze sensor signals from at least one sensor based on at least one geographic location of the user. In this way, the user's geographic location can be considered in signal analysis and therefore in quality determination. If the user's geographic location changes, the motion type "carrying while walking" is more likely to be inferred. If the user's geographic location does not change, the motion type "lifting mass" is more likely to be inferred. This simplifies the association assignment between relevant patterns in the sensor signals and existing signal or frequency examples.

[0033] Advantageously, the computing unit can be specifically configured to control at least one sensor of the technical device to measure and / or determine at least one vital parameter of the user and / or at least one geographical location. In this way, the resources of the technical device can be advantageously utilized to enable advanced functions on the technical device.

[0034] It is equipped with multiple sensors for measuring the user's vital signs, and these measurements can be controlled by a computing unit. These include: a heart rate sensor (which measures the user's heart rate by detecting, for example, pulsations in blood vessels); an electrocardiogram (ECG) sensor (which measures the electrical activity of the heart); a blood pressure sensor (which measures blood pressure in arteries); a pulse oximeter (which measures blood oxygen saturation); and a temperature sensor (which measures the user's body temperature).

[0035] In wearable smartwatches, the following sensors can be used to measure a user's vital signs: Heart rate sensor: This sensor measures the user's heart rate. Accelerometer: This sensor can be used to track the user's movement and estimate their activity level. Gyroscope: This sensor can be used to track the user's orientation and movement. GPS sensor: This sensor can be used to locate the user and calculate the distance traveled. Barometer: This sensor can be used to measure the user's altitude, which can be used to estimate activity level. The smartwatch according to this disclosure may also have sensors such as ECG, blood pressure, and temperature sensors, which can be used to monitor the user's vital signs more closely.

[0036] Furthermore, the computing unit may be specifically configured to initiate and / or execute at least one training run to acquire at least one calibration signal from at least one sensor for a user moving without mass, preferably used to calibrate and / or normalize sensor signals from at least one sensor for a user moving with mass. To initiate the training run, it is advantageously specified that the computing unit prompts the user to actively begin the training run. To allow the user to initiate the training run, manual input, key operation, or a similar method may be provided, for example. To initiate the training run, it may also be specified that the computing unit can automatically initiate the training run, for example, when a specific event occurs, such as when the technology device is activated, when a mass determination function is activated, and / or when a new user logs into the technology device. To perform the training run, the computing unit may automatically trigger the training run by, for example, correspondingly controlling the sensors and / or interactively guiding the user through the training run, to support the training run.

[0037] Furthermore, the computing unit may be specifically configured to initiate and / or perform at least one or more measurements to obtain different signal examples or frequency examples of sensor signals (especially calibrated and / or normalized) of corresponding quality from at least one sensor, preferably for at least one user or for multiple different users. In this way, training data (e.g., for artificial neural networks) can be collected. On the other hand, data for feature plots can be collected. Different signal examples or frequency examples of sensor signals (especially calibrated and / or normalized) of corresponding quality from at least one sensor can advantageously assist in finding known patterns in new sensor signals and assigning corresponding quality to the found patterns.

[0038] Advantageously, the technical device may also include at least one user-side interface. The at least one user-side interface may, for example, include a display. Furthermore, the at least one user-side interface may also include buttons, keys, touch-sensitive surfaces, etc.

[0039] According to another advantage, the computing unit can be specifically configured to control at least one user-side interface, such that, with user assistance, at least one training run can be started and / or executed to calibrate and / or normalize sensor signals from at least one sensor for a user moving with mass (preferably for at least one user or for multiple different users). In this way, the user can participate in the construction of the proposed function. Advantageously, the computing unit is thus able to interact with the user through at least one user-side interface.

[0040] Furthermore, it is advantageous that the computing unit is specifically configured to control at least one user-side interface, enabling, with user assistance, to initiate and / or execute at least one or more measurements to obtain different signal examples or frequency examples of sensor signals (especially calibrated and / or normalized) from at least one sensor of corresponding quality, preferably for at least one user or for multiple different users. In this way, the user can also participate in the construction of the proposed functionality. Advantageously, the computing unit is thus able to interact with the user through at least one user-side interface.

[0041] Another valuable advantage is that the computing unit is specifically designed to create and / or analyze load curves, preferably for at least one user or for multiple different users. In these ways, the practicality and functionality of the technical equipment can be significantly enhanced.

[0042] Furthermore, it is conceivable that load curves could be created periodically (e.g., when the mass is lifted and / or carried), periodically (e.g., daily), and / or in an event-driven manner (e.g., by activation, such as by a user). In this way, the usability and functionality of the technological device can become flexible.

[0043] For communication with the user, at least one user-side interface may be provided, which may include, for example, a display (preferably a touch display) and / or at least one microphone. Preferably, the at least one user-side interface is specifically configured to acquire user input, particularly voice commands and / or touch commands, which can be used to initiate quality determination. For this purpose, the at least one user-side interface may control a computing unit upon corresponding user input to determine at least one mass lifted and / or carried by the user and / or at least one load curve of the user.

[0044] To improve the usability of the technology device, at least one user-side interface may be implemented at least partially in an external device, such as the user's mobile device, like a smartphone, smart glasses, head-up display, virtual reality device, augmented reality device, and / or hybrid technology device.

[0045] To visualize the functionality of the technical device, the computing unit can be specifically configured to control at least one user-side interface to output at least one specific mass lifted and / or carried by the user and / or at least one load curve of the user. In this way, the functionality of the technical device becomes easy to understand.

[0046] To further improve the functionality of the technical device, the computing unit can be specifically configured to control at least one user-side interface to output acoustic, optical, and / or tactile feedback to the user when at least one quality and / or at least one load curve exceeds a specific risk threshold for the user. Advantageously, the feedback may include at least one or more warning levels. Advantageously, the risk threshold may be determined based on at least one vital parameter of the user. For this purpose, the computing unit can perform special calculations. With the feedback provided to the user, the user can be warned and protected from health hazards.

[0047] To further enhance the functionality of the technological device, the computing unit can be specifically configured to: communicate at least one defined mass lifted and / or carried by the user and / or at least one load curve of the user to the user's mobile device and / or healthcare provider (e.g., employer and / or doctor). In this way, the technological device can become an integral part of a system that protects or even promotes the user's health.

[0048] To further enhance the functionality of the technical equipment, the computing unit can be specifically configured to issue an emergency call when at least one mass and / or at least one stress curve exceeds a specific risk threshold for the user. The emergency call can be sent to caregivers, doctors, hospitals, and / or emergency rescue services, thereby ensuring that the user receives assistance in emergency situations.

[0049] According to another aspect of the invention, it is generally proposed that a technical device, especially a smart device (e.g., which can be configured as described above), be used to determine at least one mass lifted and / or carried by a user of the technical device. In this regard, the invention recognizes that such technical devices are typically worn close to the user's body, such as on the wrist, chest, head, or other body parts, and have sensors capable of sensing the user's motion dynamics and / or motion properties. The invention also recognizes that motion detection using physical laws can enable the determination of the mass or weight lifted and / or carried. The invention utilizes these findings and proposes the use of technical devices for mass determination. Technical devices can be used for everyday use, such as measuring the weight of suitcases, crates, carrying boxes, etc. Technical devices can be used for kitchen assistance, such as for weighing certain foods. Technical devices can be used for various purposes, such as for controlling overload of transport equipment, vehicles, etc. Technical devices can also be used to estimate the weight of a child lifted and / or carried on the arm. Technical devices can be used as fitness equipment for measuring mass, such as when lifting weights in a gym. Technical devices can be used as health monitoring devices for measuring mass, such as in work environments involving lifting and / or carrying heavy objects.

[0050] According to another aspect of the invention, it is generally proposed that technical devices, especially intelligent devices (e.g., which can be configured as described above), be used to measure liquid intake.

[0051] According to one aspect, the present invention provides a method for a user to determine at least one mass that can be lifted and / or carried by him, the method being implemented by means of a technical device (preferably in the form of a smartwatch and / or a sports wristband) comprising at least one sensor configured to measure at least one dynamic motion parameter, wherein sensor signals from the at least one sensor are processed and / or analyzed to determine at least one mass lifted and / or carried by the user. This method achieves the same advantages described above associated with the technical device. These advantages are fully incorporated herein by reference.

[0052] Advantageously, frequency analysis methods can be used to process the sensor signal of at least one sensor, especially calibrated and / or normalized sensor signals, to determine at least one quality level. In this way, well-established methods in frequency analysis can be used to process and / or analyze the sensor signal of at least one sensor.

[0053] To determine at least one quality, the frequency, amplitude, and / or shape of a sensor signal (especially after calibration and / or normalization) from at least one sensor can be analyzed, thereby determining the quality, particularly based on the frequency, amplitude, and / or shape (especially transients and / or slope) of the sensor signal (especially after calibration and / or normalization) from at least one sensor. In this way, patterns in the sensor signal that may indicate the determined quality can be detected.

[0054] For simplicity, at least one filter, such as a high-pass filter and / or a low-pass filter, can be used to determine whether a user is carrying and / or lifting a mass. If lifting and / or carrying a mass is associated with certain excessively high and / or low frequencies, such frequencies can be safely and reliably detected by a simple electrical circuit in the form of a filter.

[0055] On the one hand, it is conceivable to determine quality by processing different signal examples or frequency examples of sensor signals (especially calibrated and / or normalized) from at least one sensor using feature maps. In this way, corresponding quality can be simply and quickly assigned to similar signal examples or frequency examples.

[0056] On the other hand, it is conceivable to use artificial neural networks to process the sensor signals of at least one sensor, especially calibrated and / or normalized sensor signals, to determine quality. In this way, simple and rapid analysis of sensor signals can be achieved.

[0057] Advantageously, the sensor signal of at least one sensor can be calibrated and / or normalized based on at least one vital parameter of the user, said vital parameter particularly including: weight, height, body density, BMI, pulse, heart rate, blood volume, blood pressure, blood oxygen saturation, and / or body temperature. In this way, quality determination can be user-specific.

[0058] Furthermore, sensor signals from at least one sensor can be processed and / or analyzed based on at least one of the user's geographical locations. In this way, in particular, the user's movement type can be verified. For example, this can identify whether the user is lifting mass and / or carrying mass while walking. This improves the processing and / or analysis of sensor signals.

[0059] Advantageously, at least one sensor of the technical device can be used to measure and / or determine at least one vital parameter of the user and / or at least one geographical location. In this way, the existing resources of the technical device can be used in an improved manner.

[0060] To improve the quality determination, it can be specified that at least one training process is performed to obtain at least one calibration signal for the at least one sensor when the user moves without mass. Preferably, the calibration signal can be used to calibrate and / or normalize the sensor signal of the at least one sensor when the user moves with mass. Therefore, the quality determination can be precise and / or user-specific.

[0061] To achieve accurate quality determination, it can be specified that at least one or more measurements are performed to obtain signal or frequency examples of sensor signals (especially calibrated and / or normalized) from at least one sensor for the corresponding quality, preferably for at least one user or for multiple different users. In this way, training data and / or feature map data can be obtained for signal analysis.

[0062] This method can be used to create and / or analyze load curves. Load curves are preferably created for at least one user or multiple different users. In terms of control techniques, it is conceivable that load curves are created periodically (e.g., when a mass (m) is lifted and / or carried), periodically (e.g., daily), and / or in an event-driven manner (e.g., by activation, such as by a user).

[0063] This method can be used to create and / or analyze motion curves to measure fluid intake. The motion curves are preferably created for at least one user or multiple different users. In terms of control techniques, it is conceivable that load curves are created periodically (e.g., when the mass is lifted and / or carried), periodically (e.g., daily), and / or in an event-driven manner (e.g., by activation, such as by a user).

[0064] To make the function identifiable by the user, it may be specified that at least one defined mass lifted and / or carried by the user and / or at least one load curve and / or measured fluid intake be output to the user.

[0065] To begin determining at least one mass that the user lifts and / or carries, user input, particularly voice commands and / or touch commands, can be collected. Furthermore, user input, particularly voice commands and / or touch commands, can be collected to begin creating and / or analyzing at least one load profile for the user and / or to begin measuring fluid intake. Thus, the user can actively use the technical equipment to achieve the goal of determining mass and / or creating and / or analyzing load profiles.

[0066] For safety and / or health promotion reasons, it may be specified that auditory, visual, and / or tactile feedback be provided to the user when at least one determined quality and / or at least one created load curve exceeds a specific risk threshold for the user. Advantageously, this feedback may include at least one or more warning levels.

[0067] To increase the practicality of the method, it may be specified that at least one defined mass lifted and / or carried by the user and / or at least one load curve of the user are transmitted to the user's mobile device and / or medical service provider (e.g., employer and / or physician).

[0068] For security reasons, it may be stipulated that an emergency call be issued when at least one determined quality and / or at least one created load curve exceeds a specific risk threshold for the user.

[0069] According to another aspect, the present invention provides a computer program product comprising instructions that, when a computer executes the computer program, cause the computer program to perform methods possibly described above. This computer program product achieves the same advantages described above related to the technical devices and / or methods. These advantages are fully incorporated herein by reference.

[0070] According to another aspect, the present invention provides a storage medium on which a corresponding computer program product is stored. This storage medium enables the realization of the same advantages described above related to the technical devices and / or methods. These advantages are fully incorporated herein by reference.

[0071] According to another aspect, the present invention provides a data carrier signal for transmitting a corresponding computer program product. This data carrier signal enables the realization of the same advantages described above related to the technical equipment and / or methods. These advantages are fully incorporated herein by reference.

[0072] According to another aspect, the present invention provides a computing unit for determining at least one mass that can be lifted and / or carried by a user, comprising a storage device (which can be understood as the storage unit as described above) and a computing device (which can be understood as the computing unit as described above), wherein code is stored in the storage device, and wherein, when the computing device executes the code, a method that may be executed as described above is performed. With this computing device, the same advantages associated with the aforementioned technical devices and / or methods can be achieved. These advantages are fully described herein.

[0073] Advantageously, the computing unit can be specifically adapted for use in a technical device (which may be implemented as described above) to process sensor signals from the sensor and, based on the processing, determine at least one mass that the user lifts and / or carries.

[0074] To enhance computing power and capacity by implementing this method, the storage and computing devices can be implemented, at least in part, on external devices such as cloud computers, computing centers, etc.

[0075] Other advantages, features, and details of the present invention are derived from the following detailed description of several embodiments of the invention with reference to the accompanying drawings. Wherein: Figure 1 A schematic diagram of the technical equipment is shown. Figure 2 A schematic representation showing sensor signals and corresponding signal or frequency examples.

[0076] Figure 1 and Figure 2Used to explain the concept of this invention.

[0077] According to one aspect, a technical device 100 is proposed, particularly a smart device, preferably in the form of a smartwatch and / or wristband that can be worn by user B near the body (preferably on the wrist). The smart device 100 is specifically designed to determine at least one mass m that can be lifted and / or carried by user B.

[0078] Technical equipment 100 has the following components: - At least one sensor, particularly a motion sensor 10 configured to measure at least one dynamic motion parameter, and - Calculation unit 30, The computing unit 30 is specifically configured to process and / or analyze sensor signals from at least one sensor 10, and to determine, based on the processing and / or analysis, at least one mass m that user B lifts and / or carries.

[0079] In principle, the technology equipment 100 can realize various smart devices that user B can carry, such as mobile phones, smartwatches, (sports) wristbands, (sports) headbands, (sports) ankle straps, (sports) chest straps, rings, heart rate monitors, smart clothing, etc.

[0080] In the context of this disclosure, sensor 10 (especially a motion sensor) may include any sensor capable of sensing dynamic motion parameters of user B (e.g., position, velocity, and / or acceleration). Sensor 10 may include any sensor capable of measuring position, motion, acceleration, tilt, force, rotation, etc. Sensor 10 may be implemented as an electromechanical sensor and / or an electromagnetic sensor and / or an electro-optical sensor. Sensor 10 may sense at least one dynamic motion parameter in at least two or three directions, or may be implemented as a 2D or 3D sensor.

[0081] In the context of this disclosure, mass m can refer to an object having a specific weight. However, mass m can also be used as a synonym for load and / or weight.

[0082] Technical equipment 100 is used to quickly and easily determine mass m without the need for a separate weighing instrument.

[0083] The concept here is to collect and analyze motion dynamics and / or motion types as a mass m is moved (e.g., lifted by hand or "weighed" in the hand, i.e., moved up and down). Such characteristic motions can be modeled and learned using an artificial neural network (KNN). These characteristic motions can be modeled for at least one mass m or for different masses m. Such characteristic motions can be subject to user-specific analysis, for example, by calibrating and / or normalizing sensor signals. Calibration can be performed in a user-specific manner. For this purpose, a training run can be used, for example, which can be performed by technical device 100.

[0084] The technology device 100 is universally applicable. Advantageously, the technology device 100 is always on the person or readily available, for example, when a smartwatch is worn on the wrist.

[0085] With the help of the technology device 100, comprehensive practicality and enhanced functionality can be provided. Advantageously, the smart device 100 can be further developed into an assistant for analyzing personal load when lifting and carrying heavy objects.

[0086] The technology device 100 can record conscious and unconscious lifting and carrying activities (e.g., throughout the day), accumulate them, and advantageously send warning messages when the load is too high. In this way, users can improve their self-awareness of lifting and carrying heavy objects in daily life and, when necessary, use their actions to prevent diseases such as herniated discs and back problems.

[0087] The technology device 100 can also create load curves and transmit them to healthcare providers, such as employers and / or physicians.

[0088] Interaction with user B can be achieved using technical device 100. User B can be informed of the determined mass m and / or the applied load curve. User B can be invoked to calibrate sensor signals and / or create different signal examples and / or frequency examples, particularly for at least one mass, or advantageously for multiple different masses.

[0089] The technical device 100 can be implemented with a user-side interface 40, which can be controlled by the computing unit 30.

[0090] The technical device 100 can be designed for personal use by a specific user. The technical device 100 can also be used as a general measuring tool, for example, in a gym or doctor's office.

[0091] In addition, the technical device 100 may include a storage unit 20.

[0092] On one hand, storage unit 20 may include a permanent storage area. This permanent storage area can, for example, store code that can be used to execute a corresponding process for determining mass m. Furthermore, this permanent storage area can be used to store feature maps K and / or artificial neural networks KNN, thereby enabling simple and efficient analysis of detected motion dynamics and / or motion types.

[0093] Secondly, the storage unit 20 may have a volatile storage area. The volatile storage area may, for example, be used as the working memory of the computing unit 30.

[0094] Storage unit 20 may have at least one first storage area 21, particularly permanent, in which a feature map K can be stored. This feature map K can map the sensor signal (particularly calibrated and / or normalized) of at least one sensor 10 to different signal examples and / or frequency examples f for a given mass m (see [link to relevant documentation]). Figure 2 ).

[0095] Different signal examples and / or frequency examples f of the sensor signal of at least one sensor 10 (especially after calibration and / or normalization) of corresponding quality m can be individually stored for at least one user B, or universally stored for multiple different users B.

[0096] Different signal examples and / or frequency examples f of the sensor signal (especially calibrated and / or normalized) of at least one sensor 10 with a corresponding mass m can be stored for different motion types. This means that different motion types, such as one motion type (lifting) and / or another motion type (carrying while walking), can be distinguished during the processing and / or analysis of the sensor signal.

[0097] Furthermore, storage unit 20 may have at least one second storage area 22, particularly permanent, in which an artificial neural network (KNN) can be stored (preferably self-learning, more preferably trained or trainable). The KNN may have been specifically trained, or may be trainable, to infer a corresponding quality m based on the sensor signal of at least one sensor 10 (particularly calibrated and / or normalized). The KNN can perform analysis of signal progression and / or signal frequency. The KNN can be trained to identify patterns in the sensor signal of at least one sensor 10, including amplitude patterns, frequency patterns, signal shape, etc. For example, the KNN can be trained on a dataset of signal examples and / or frequency examples, and subsequently used to identify similar signal examples and / or frequency examples in new sensor signals and assign them to specific qualities. Advantageously, the KNN can implement Fourier transform and wavelet transform, which can be advantageously used to analyze the frequency components of the sensor signal. Advantageously, the KNN can implement signal processing methods to extract information, such as amplitude, frequency, waveform, etc., from the sensor signal.

[0098] The computing unit 30 preferably includes an electronic unit specifically configured to process sensor signals from at least one sensor 10, particularly calibrated and / or normalized sensor signals, using at least one frequency analysis method. The electronic unit may include electrical components and / or electronic elements that can be used for analog signal conditioning, digital signal processing, and / or evaluation.

[0099] The computing unit 30 is preferably specifically configured to analyze the frequency, amplitude, and / or shape of the sensor signal (especially after calibration and / or normalization) of at least one sensor 10. Motion dynamics and / or motion type can be described by means of the frequency, amplitude, and / or shape of the sensor signal (especially after calibration and / or normalization) of the sensor 10. Motion dynamics and / or motion type can be specific to the mass m of the moving object carried by user B while lifting or walking. For example, if the user is walking while carrying mass m, a relatively heavier mass m may result in a higher amplitude in the sensor signal compared to a lighter mass m. For example, if the user is running while carrying mass m, a relatively heavier mass m may show a lower frequency in the sensor signal compared to a lighter mass m. For example, if user B is lifting mass m, a fairly flat transient process in the sensor signal may indicate that a relatively heavy mass is being lifted. A fairly steep transient process in the sensor signal may indicate that a relatively light mass m is being lifted.

[0100] For convenience, the computing unit 30 may include at least one electrical filter (e.g., a high-pass filter and / or a low-pass filter) to detect signals with specific frequencies, thereby determining whether user B is carrying and / or lifting mass m. With the help of filters, thresholds can be easily and quickly set for different signal frequencies.

[0101] On the one hand, the computing unit 30 may be specifically configured to process and / or analyze different signal examples and / or frequency examples f of the sensor signal of at least one sensor 10 (especially after calibration and / or normalization) with the aid of the characteristic curve graph K to determine the quality m.

[0102] Alternatively or additionally, the computing unit 30 may be specifically configured to use an artificial neural network KNN to process and / or analyze the sensor signal of at least one sensor 10, especially the calibrated and / or normalized sensor signal, to determine the quality m.

[0103] Advantageously, the computing unit 30 can be specifically configured to process or analyze the sensor signals of the sensor 10 based on at least one vital parameter of user B. At least one vital parameter may include, for example, weight, height, body density, BMI, pulse, heart rate, blood volume, blood pressure, blood oxygen saturation, and / or body temperature. In this way, user-specific sensor signal analysis can be achieved, which can be individualized for a specific user B. Users B with different physiques and / or levels of training may lift and / or carry comparable masses m in different ways. For example, a trained furniture mover may carry heavy objects more easily than a clerk who does not frequently lift and / or carry heavy objects due to occupational reasons (e.g., office work). However, the immediate state of the same user (e.g., after rest and recovery or after a long workday) may also affect the sensor signals. Such user-specific factors can be advantageously mapped using vital parameters and taken into account when determining mass. Different vital parameters of user B can be provided, for example, as additional inputs to an appropriately trained neural network and / or as additional data to a feature map.

[0104] When processing and / or analyzing sensor signals from at least one sensor 10, the computing unit 30 may consider at least one geographical location of user B. If user B's geographical location changes over time, the movement type "carrying while walking" is more likely to be inferred. If the user's geographical location does not change, the movement type "lifting mass" is more likely to be inferred.

[0105] Preferably, the computing unit 30 may be specifically configured to: appropriately control at least one sensor of the technical device 100 to measure at least one vital sign and / or at least one geographical location of user B.

[0106] The computing unit 30 can also be specifically configured to initiate and / or execute at least one training run. To initiate a training run, the computing unit 30 can prompt user B, for example via a user-side interface 40, to enable or start the training run, such as through manual input or key operation. The computing unit can also automatically start the training run, for example when a specific event occurs (such as when the technical device 100 is activated, when the function to determine quality m is enabled, and / or when a new user B logs into the technical device 100). To execute the training run, the computing unit 30 can automatically control the progress of the training run (e.g., through corresponding control sensors 10 and / or through interactive guidance for the user to complete the training run).

[0107] The computing unit 30 may also be specifically configured to initiate and / or perform at least one or more measurements to obtain different signal examples or frequency examples f of the sensor signal (especially calibrated and / or normalized) of at least one sensor 10 for a corresponding mass m. The signal examples or frequency examples f are preferably executed for at least one user B or for multiple different users B.

[0108] As described above, the technical device 100 may further include at least one user-facing interface 40. For example... Figure 1 As shown, at least one user-facing interface 40 may include, for example, a display. At least one user-facing interface 40 may also include buttons, keys, touch-sensitive surfaces, etc.

[0109] The computing unit 30 may be specifically configured to control at least one user-side interface 40, enabling user B to initiate and / or execute at least one training run.

[0110] The computing unit 30 may be specifically configured to control at least one user-side interface 40, enabling user B to initiate and / or perform at least one or more measurements.

[0111] According to a further advantage, the calculation unit 30 can be specifically configured to create and / or analyze load curves, preferably for at least one user B or for multiple different users B. Load curves can be created periodically (e.g., when mass m is lifted and / or carried), periodically (e.g., daily), and / or in an event-driven manner (e.g., by activation, such as by user B).

[0112] To visualize the functionality of the technology device 100, the computing unit 30 may be specifically configured to control at least one user-facing interface 40 to output at least one specific mass m lifted and / or carried by user B and / or at least one load curve of user B.

[0113] At least one user-facing interface 40 may preferably be configured to: begin determining at least one specific mass m lifted and / or carried by user B, and / or create and / or analyze at least one load curve of user B.

[0114] To further enhance the functionality of the technical device 100, the computing unit 30 may be specifically configured to control at least one user-facing interface 40 to output auditory, visual, and / or tactile feedback to user B when at least one mass m and / or at least one load curve exceeds a specific risk threshold for user B. Advantageously, this feedback may include at least one or more warning levels. Advantageously, the risk threshold may be determined based on at least one vital parameter of the user, preferably by the computing unit 30.

[0115] To further enhance the functionality of the technology device 100, the computing unit 30 may be specifically configured to: communicate at least one specific mass m lifted and / or carried by user B and / or at least one load curve of user B to user B's mobile device and / or to a healthcare provider (e.g., employer and / or physician).

[0116] To further enhance the functionality of the technical equipment 100, the computing unit 30 may be specifically configured to issue an emergency call when at least one mass m and / or at least one load curve exceeds a specific risk threshold for user B.

[0117] Figure 2 This is merely a illustrative example of a sensor signal from an object, for example, being lifted and weighed, having a determined mass *m*. Assuming the force *F(s,t)* is a characteristic property of the motion, where the mass is at position *s* at time *t*, the signal example can be determined. The signal example can be supplemented by experiments with multiple test subjects and objects. The obtained signal example can be used to train a neural network KNN that can determine the mass *m* for each event (a single up-and-down movement) with an appropriate confidence interval. Similar sensor signal examples can be created for objects carrying a determined mass *m* during walking and fed into the neural network KNN for training.

[0118] According to another aspect of the invention, this technical solution provides a corresponding use of a corresponding technical device 100 to determine at least one mass m lifted and / or carried by user B of the technical device 100. According to another aspect of the invention, this technical solution provides a corresponding method for using a corresponding smart device 100 to determine at least one mass m that can be lifted and / or carried by user B. According to another aspect of the invention, this technical solution provides: a corresponding computer program product for executing the corresponding method; a corresponding storage medium on which the corresponding computer program product is stored; and a corresponding data carrier signal for transmitting the corresponding computer program product. According to another aspect of the invention, this technical solution provides a corresponding computing unit 30 for executing the corresponding method, wherein, in particular, the computing unit can be used in the corresponding technical device 100.

[0119] The foregoing description of the embodiments illustrates the invention only in the context of examples. Of course, the various features of the embodiments can be freely combined with each other without departing from the scope of the invention, provided it is technically reasonable.

[0120] List of reference numerals 10 sensors 20 storage units 21 Storage Area 22 Storage Area 30 computing units 40 Interface 100 Smart Devices f Frequency example m mass User B K Feature Map KNN artificial neural network

Claims

1. A method for determining at least one mass (m) lifted and / or carried by a user (B) using a technical device (100), said technical device (100) being in the form of a smartwatch and / or a sports wristband, in, The technical device (100) has at least one sensor (10) configured to measure at least one dynamic motion parameter. The sensor signals of the at least one sensor (10) are processed and / or analyzed to determine at least one mass (m) lifted and / or carried by the user (B).

2. The method according to the preceding claim, wherein, The sensor signal of the at least one sensor (10), especially the calibrated and / or normalized sensor signal, is processed using a frequency analysis method to determine at least one quality (m).

3. The method according to any one of the preceding claims, wherein, In order to determine the at least one mass (m), the frequency, amplitude and / or shape of the sensor signal of the at least one sensor (10), which is in particular calibrated and / or normalized, is analyzed, thereby determining the mass (m) in particular based on the frequency, amplitude and / or shape, especially transient and / or slope, of the sensor signal of the at least one sensor (10), which is in particular calibrated and / or normalized.

4. The method according to any one of the preceding claims, wherein, At least one filter is used to determine whether the user (B) is carrying and / or lifting mass (m), the at least one filter being, for example, a high-pass filter and / or a low-pass filter.

5. The method according to any one of the preceding claims, wherein, The quality (m) is determined by processing different signal examples and / or frequency examples (f) of the sensor signal of the at least one sensor (10), especially calibrated and / or normalized, with the aid of a family of characteristic curves (K).

6. The method according to any one of the preceding claims, wherein, The quality (m) is determined by processing the sensor signal of the at least one sensor (10), especially the calibrated and / or normalized sensor signal, with the aid of an artificial neural network (KNN).

7. The method according to any one of the preceding claims, wherein, Based on at least one vital parameter of the user (B), the sensor signal of the at least one sensor (10) is calibrated and / or normalized, the at least one vital parameter including in particular: weight, height, body density, BMI index, pulse, heart rate, blood volume, blood pressure, blood oxygen saturation and / or body temperature.

8. The method according to any one of the preceding claims, wherein, Based on at least one geographical location of the user (B), the sensor signals of the at least one sensor (10) are processed and / or analyzed.

9. The method according to any one of the preceding claims, wherein, Control at least one sensor of the technical device (100) to measure and / or determine at least one vital parameter and / or at least one geographic location of the user (B).

10. The method according to any one of the preceding claims, wherein, Perform at least one training run to obtain at least one calibration signal for the at least one sensor (10) when the user (B) moves without mass (m), the calibration signal preferably being used to calibrate and / or normalize the sensor signal of the at least one sensor (10) when the user (B) moves with mass (m).

11. The method according to any one of the preceding claims, wherein, Perform at least one or more measurements to obtain different signal examples and / or frequency examples (f) of the sensor signal of the at least one sensor (10) for the corresponding mass (m), especially calibrated and / or normalized, preferably for at least one user (B) or for multiple different users (B).

12. The method according to any one of the preceding claims, wherein, Create and / or analyze load curves, preferably for at least one user (B) or for multiple different users (B), wherein load curves are created in particular in the following manner: in a periodic manner, for example when lifting and / or carrying mass (m); in a periodic manner, for example daily; and / or in an event-controlled manner, for example by activation, for example by said user (B).

13. The method according to any one of the preceding claims, wherein, Create and / or analyze motion profiles for measuring fluid intake, preferably for at least one user (B) or multiple different users (B), wherein the motion profiles are created in particular in the following manner: in a periodic manner, for example when lifting and / or carrying a mass (m); in a periodic manner, for example daily; and / or in an event-controlled manner, for example by activation, such as by said user (B).

14. The method according to any one of the preceding claims, wherein, Collect user input, especially voice commands and / or touch commands, to begin determining at least one mass (m) lifted and / or carried by the user (B), and / or to begin creating and / or analyzing at least one load curve of the user (B), and / or to begin measuring fluid intake.

15. The method according to any one of the preceding claims, wherein, Output to the user (B) at least one specific mass (m) lifted and / or carried by the user (B) and / or at least one load curve of the user (B) and / or the measured liquid intake.

16. The method according to any one of the preceding claims, wherein, If at least one determined quality (m) and / or at least one created load curve exceeds a specific risk threshold for the user (B), then auditory, visual, and / or tactile feedback is output to the user (B), wherein, in particular, the feedback may include at least one or more warning levels.

17. The method according to any one of the preceding claims, wherein, At least one determined mass (m) lifted and / or carried by the user (B) and / or at least one load curve of the user (B) are transmitted to the user (B)’s mobile device and / or healthcare provider, such as an employer and / or physician.

18. The method according to any one of the preceding claims, wherein, An emergency call is issued if at least one determined quality (m) and / or at least one created load curve exceeds a specific risk threshold for the user (B).

19. A computer program product comprising instructions that, when a computer executes the computer program, cause the computer program to perform the method according to any one of the preceding claims.

20. A storage medium having a computer program product according to the preceding claim stored thereon.

21. A calculation unit (30) for determining at least one mass (m) lifted and / or carried by a user (B). It includes storage devices and computing devices. in, The storage device stores code. Furthermore, when the computing device executes the code, it performs the method according to any one of the preceding claims.

22. A technical device (100) for a user (B) to determine at least one mass (m) lifted and / or carried by the user (B), the technical device (100) being in the form of a smartwatch and / or wristband, the technical device (100) comprising: - At least one sensor (10), particularly a motion sensor, configured to measure at least one dynamic motion parameter, and - Computation unit (30), particularly the computation unit according to the preceding claim, The computing unit (30) is specifically configured to process and / or analyze the sensor signals of the at least one sensor (10) and determine the at least one mass (m) lifted and / or carried by the user (B) based on the processing and / or analysis.

23. The use of the technical device (100) according to the preceding claim for determining at least one mass (m) lifted and / or carried by a user (B) of the device (100).

24. The use of the technical device (100) according to claim 22 is for measuring liquid intake.

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