System and method for detecting defecation events

Abstract

The systems and methods facilitate detecting and recording bowel events in subjects, such as participants in clinical trials for therapeutics to treat gastrointestinal disorders such as irritable bowel syndrome (IBS), inflammatory bowel disease (IBD), and chronic constipation. The systems and methods can also be used by individual patients to detect and record bowel events, and the resulting data can be reviewed by health care providers when evaluating the patient's gastrointestinal health and / or treatment.

Description

Technical Field

[0001] The present disclosure relates to a system and method for automatically detecting a target defecation event. More specifically, the present disclosure relates to a system and method for detecting a defecation event by detecting one or more actions associated with such an event.

Background Art

[0002] Accurately obtaining defecation event data (e.g., the frequency and / or timing of such events) is typically required for the diagnosis, evaluation, treatment, and / or management of various gastrointestinal diseases such as irritable bowel syndrome (IBS), inflammatory bowel disease (IBD), and chronic constipation. However, obtaining such data can be cumbersome and often depends on the patient's memory, which can be inaccurate.

Summary of the Invention

[0003] The present disclosure provides a system and method for easily and accurately detecting a defecation event and obtaining defecation event data. These systems and methods do not need to rely on the patient's memory.

[0004] According to one embodiment of the present disclosure, a system for detecting a target defecation event includes a wearable device configured to be carried on a target's body. The wearable device is operable in a sleep mode and an active mode. The wearable device includes a wake-up sensor configured to detect a first stimulus and a mechanomyogram (MMG) sensor configured to detect a target's abdominal muscle movement signal. A processor is operably coupled to the wake-up sensor and the MMG sensor. The processor reconfigures the wearable device from the sleep mode to the active mode based on the first stimulus detected by the wake-up sensor. In the active mode, the wearable device is configured to communicate with the processor and determine the occurrence of a target defecation event based on the abdominal muscle movement signal detected by the MMG sensor.

[0005] In some embodiments, the target's abdominal muscle movement signal is a second stimulus, and the system further includes a third sensor operably coupled to the processor and configured to detect a third stimulus. In the active mode, the processor is configured to determine the occurrence of a target defecation event based on the abdominal muscle movement signal detected by the MMG sensor and the third stimulus detected by the third sensor.

[0006] In some embodiments, the third sensor includes a gas sensor disposed within the wearable device and configured to detect flatulence.

[0007] In some embodiments, the third sensor is an audio sensor configured to detect toilet flushing sounds. In some embodiments, the audio sensor may also be configured to detect sounds caused by or associated with movement of clothing, such as rubbing when removing lower body clothing.

[0008] In some embodiments, the third sensor is an electromyogram electrode configured to detect the target's electromyogram signal.

[0009] In some embodiments, the third sensor is an inertial measurement unit configured to detect changes in the posture of the subject.

[0010] In some embodiments, the wearable device further includes a patch configured to be carried on the body of the subject, and the patch carries a wake-up sensor and an electromyogram sensor.

[0011] In some embodiments, the patch further carries a processor.

[0012] In some embodiments, the wearable device further includes a belt configured to extend around the body of the subject, and the belt carries a wake-up sensor and an electromyogram sensor.

[0013] In some embodiments, the belt further carries a processor.

[0014] In some embodiments, the wake-up sensor includes one of an optical sensor and a resistance sensor configured to detect when the subject removes lower body clothing.

[0015] In some embodiments, the wearable device further includes a health sensor configured to detect health stimuli associated with the health of the subject.

[0016] In some embodiments, the health sensor includes a blood sensor configured to detect blood in the feces of the subject.

[0017] In some embodiments, the blood sensor includes a solid vapor detection sensor configured to detect one or more volatile organic compounds.

[0018] According to another embodiment of the present disclosure, a system for detecting a defecation event of a subject includes a wearable device configured to be carried on the subject's torso. The wearable device includes an electromyogram sensor configured to detect the subject's abdominal muscle movement signal and a gas sensor configured to detect flatulence. A processor is operably coupled to the electromyogram sensor and the gas sensor. The processor is configured to determine the occurrence of a defecation event of the subject based on the abdominal muscle movement signal detected by the electromyogram sensor and the flatulence detected by the gas sensor.

[0019] In some embodiments, the processor is configured to determine the occurrence of a defecation event of the subject based on a series of events including that one of the abdominal muscle movement signal detected by the electromyogram sensor and the flatulence detected by the gas sensor precedes the other of the abdominal muscle movement signal detected by the electromyogram sensor and the flatulence detected by the gas sensor.

[0020] In some embodiments, the wearable device further includes a base carrying the electromyogram sensor, the gas sensor, and the processor.

[0021] According to yet another embodiment of the present disclosure, a system for detecting a defecation event of a subject includes a wearable device configured to be carried on the subject's torso and under the lower body clothing worn by the subject. The wearable device includes an optical sensor configured to detect increased light when the subject removes the lower body clothing. A processor is operably coupled to the optical sensor, and the processor is configured to determine the occurrence of a defecation event based at least in part on the increased light detected by the optical sensor.

[0022] In some embodiments, the wearable device is operable in an active mode and a sleep mode, and the processor is configured to reconfigure the wearable device from the sleep mode to the active mode when it determines that the subject has removed the lower body clothing in response to the increased light detected by the optical sensor.

[0023] In some embodiments, the optical sensor is a first sensor configured to detect light as a first stimulus. The wearable device further includes a second sensor configured to detect a second stimulus when the wearable device is in an active mode. The second stimulus is different from the first stimulus. The processor is operably coupled to the second sensor and is configured to determine the occurrence of a defecation event of the subject based on the signal received from the second sensor.

[0024] In some embodiments, the optical sensor is a first sensor configured to detect light as a first stimulus. The wearable device further includes a second sensor configured to detect a second stimulus when the wearable device is in an active mode. The second stimulus is different from the first stimulus. The processor is operably coupled to the second sensor and is configured to determine the occurrence of a defecation event of the subject based on the signals received from the first sensor and the second sensor.

[0025] In some embodiments, the second sensor is an electromyogram sensor.

[0026] In some embodiments, the wearable device further includes a patch configured to be carried on the body of the subject, and the patch carries the optical sensor and the processor.

[0027] In some embodiments, the wearable device further comprises a belt configured to extend around the body of the subject, and the belt carries the optical sensor and the processor.

[0028] According to yet another embodiment of the present disclosure, a method for detecting a defecation event of a subject includes detecting increased light by an optical sensor of a wearable device carried on the subject's body when the subject removes lower body clothing, detecting an abdominal muscle movement signal of the subject by a myogram sensor of the wearable device, and determining the occurrence of a defecation event based at least in part on the increased light detected when the subject removes lower body clothing and the detected abdominal muscle movement signal of the subject.

[0029] In some embodiments, the method further includes detecting flatulence by a gas sensor of the wearable device, and the determination that a defecation event has occurred is based at least in part on the detected flatulence.

[0030] In some embodiments, the method further includes detecting a sitting movement by the subject by an inertial measurement unit of the wearable device before detecting the abdominal muscle movement signal of the subject, and the determination that a defecation event has occurred is based at least in part on the detected sitting movement.

[0031] In some embodiments, the method further includes detecting a standing movement by the subject by an inertial measurement unit of the wearable device after detecting flatulence, and the determination that a defecation event has occurred is based at least in part on the detected standing movement.

[0032] In some embodiments, the method further includes detecting a plurality of defecation events of the subject over a period of time.

[0033] In some embodiments, detecting each of the plurality of defecation events of the subject includes detecting an abdominal muscle movement signal of the subject by a myogram sensor of the wearable device, detecting increased light by an optical sensor when the subject removes lower body clothing, and determining the occurrence of each of the plurality of defecation events based at least in part on the detected abdominal muscle movement signal of the subject and the increased light detected when the subject removes lower body clothing.

[0034] In some embodiments, detecting increased light when the subject removes lower body clothing by an optical sensor precedes detecting the subject's rectus abdominis motion signal by an electromyogram sensor.

[0035] In some embodiments, detecting the subject's rectus abdominis motion signal by an electromyogram sensor precedes detecting increased light when the subject removes lower body clothing by an optical sensor.

[0036] In some embodiments, the method further includes reconfiguring the wearable device from a sleep mode to an active mode based on the increased light detected when the subject removes lower body clothing, wherein in the sleep mode, the electromyogram sensor is inactive, and in the active mode, the electromyogram sensor is configured to detect the subject's rectus abdominis motion signal.

[0037] According to yet another embodiment of the present disclosure, a system for training one or more processors to detect a defecation event of a subject includes a first wearable device configured to be carried on the subject's body. The first wearable device includes a first defecation event sensor configured to detect one or more first stimuli. The system further includes a second wearable device configured to be carried on the subject's body. The second wearable device includes a second defecation event sensor configured to detect one or more second stimuli. The system further includes one or more processors operably coupled to the first defecation event sensor and the second defecation event sensor, configured to determine the occurrence of the detected defecation event of the subject based on the one or more second stimuli, and to associate the first stimuli detected by the first defecation event sensor within a predetermined period of the detected defecation event with the subject's defecation event.

[0038] In some embodiments, the one or more processors are further configured to train a machine learning algorithm for detecting the subject's defecation event using data indicative of the first stimuli detected by the first defecation event sensor associated with the subject's defecation event.

[0039] In some embodiments, the first wearable device further includes a wake-up sensor configured to detect one or more wake-up stimuli. The first wearable device is configured to transition from a sleep mode to an active mode in which the first wearable device is configured to detect a bowel movement event of the subject via a first bowel movement event sensor when the wake-up sensor detects a wake-up stimulus associated with the subject's bowel movement event. One or more processors are further configured to associate a wake-up stimulus detected by the wake-up sensor within a second predetermined period of the detected bowel movement event with the subject's bowel movement event.

[0040] In some embodiments, one or more processors comprise a first processor operably coupled to a first bowel movement event sensor and a second processor operably coupled to a second bowel movement event sensor, and the first processor and the second processor are operably coupled to each other.

[0041] In some embodiments, one or more processors consist of a single processor operably coupled to a first bowel movement event sensor and a second bowel movement event sensor.

[0042] In some embodiments, the first wearable device includes a smartwatch.

[0043] In some embodiments, the second wearable device includes a patch configured to be carried on the subject's torso.

[0044] In some embodiments, the first wearable device includes at least one of one or more processors.

[0045] In some embodiments, at least one of one or more processors communicates wirelessly with the first wearable device.

[0046] In some embodiments, the first stimulus and the second stimulus are different types of stimuli.

[0047] In some embodiments, the first stimulus and the second stimulus are the same type of stimulus.

[0048] According to another embodiment of the present disclosure, a method for training one or more processors operably coupled to a first wearable device to detect a defecation event of a subject includes detecting, by a first defecation event sensor carried by the first wearable device, one or more first stimuli; detecting, by a second defecation event sensor carried by a second wearable device, one or more second stimuli; determining, by one or more processors, the occurrence of a detected defecation event of the subject based on the second stimulus; and associating, by one or more processors, a first stimulus detected by the first defecation event sensor within a predetermined period of the detected defecation event with the defecation event of the subject.

[0049] In some embodiments, the method further includes training, by one or more processors, a machine learning algorithm for detecting a defecation event of the subject using data indicative of a first stimulus detected by a first defecation event sensor associated with the defecation event of the subject.

[0050] In some embodiments, the first wearable device further comprises a wake-up sensor configured to detect one or more wake-up stimuli, and the first wearable device is configured to transition from a sleep mode to an active mode in which the first wearable device is configured to detect a defecation event of the subject via the first defecation event sensor when the wake-up sensor detects a wake-up stimulus associated with the defecation event of the subject. The method further includes associating, by one or more processors, a wake-up stimulus detected by the wake-up sensor within a second predetermined period of the detected defecation event with the defecation event of the subject. The second predetermined period may be the same as or different from the predetermined period.

[0051] According to another embodiment of the present disclosure, a system for training one or more processors to detect a defecation event of a subject includes a wearable device configured to be carried on the subject's body. The wearable device includes a defecation event sensor configured to detect one or more stimuli. The system further includes a mobile device configured to receive user input from the subject indicating a defecation time at which the defecation event occurred. The system further includes one or more processors operably coupled to the defecation event sensor and the mobile device and configured to associate a stimulus detected by the defecation event sensor within a predetermined period of the defecation time with the subject's defecation event.

[0052] In some embodiments, the one or more processors are further configured to train a machine learning algorithm for detecting the subject's defecation event using data indicative of a first stimulus detected by a first defecation event sensor associated with the subject's defecation event.

[0053] In some embodiments, the wearable device further comprises a wake-up sensor configured to detect one or more wake-up stimuli, and the wearable device is configured to transition from a sleep mode to an active mode in which the wearable device is configured to detect the subject's defecation event via the defecation event sensor when the wake-up sensor detects a wake-up stimulus associated with the subject's defecation event. The one or more processors are further configured to associate a wake-up stimulus detected by the wake-up sensor within a second predetermined period of the detected defecation event with the subject's defecation event. The second predetermined period may be the same as or different from the predetermined period.

[0054] According to yet another embodiment of the present disclosure, a method for training one or more processors operably coupled to a wearable device to detect a target defecation event includes detecting, by a defecation event sensor carried by the wearable device, one or more stimuli. The method further includes receiving, via the target's mobile device, a user input indicating a defecation time at which the defecation event occurred. The method further includes associating, by the one or more processors, the stimuli detected by the defecation event sensor within a predetermined period of the defecation time with the target's defecation event.

[0055] In some embodiments, the method further includes training, by the one or more processors, a machine learning algorithm for detecting the target's defecation event using data indicative of a first stimulus detected by a first defecation event sensor associated with the target's defecation event.

[0056] In some embodiments, the wearable device further comprises a wake-up sensor configured to detect one or more wake-up stimuli, and the wearable device is configured to transition from a sleep mode to an active mode when the wake-up sensor detects a wake-up stimulus associated with the target's defecation event. The method further includes associating, by the one or more processors, the wake-up stimuli detected by the wake-up sensor within a second predetermined period of the detected defecation event with the target's defecation event.

Brief Description of the Drawings

[0057] The above and other advantages and objects of the present invention, and the manner of achieving them, will become more apparent and the present invention itself will be better understood by reference to the following description of embodiments of the present invention made in connection with the accompanying drawings.

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[0058] Throughout the several views, corresponding reference numerals indicate corresponding parts. The drawings represent embodiments of the invention, but the drawings are not necessarily to scale, and in order to better illustrate and explain the invention, some features may be exaggerated or omitted in some of the views.

DETAILED DESCRIPTION

[0059] Systems and methods according to embodiments of the present disclosure facilitate detecting a subject's defecation event by detecting one or more stimuli associated with the defecation event. The subject can be a participant in a clinical trial for the treatment of gastrointestinal diseases such as irritable bowel syndrome (IBS), inflammatory bowel disease (IBD), and chronic constipation. Alternatively, the systems and methods according to embodiments of the present disclosure may be used by individual patients to detect and record defecation events, and the resulting data may be reviewed by a healthcare provider when evaluating the patient's gastrointestinal health and / or treatment.

[0060] Referring now to FIG. 1, there is schematically shown a system 100 for detecting a target defecation event, according to one embodiment of the present disclosure. Generally, system 100 includes a wearable device 102 configured to be worn by, attached to, or otherwise carried by a target. Wearable device 102 detects one or more stimuli indicative of a target defecation event. Wearable device 102 is operably coupled to one or more remote devices 104 (exemplarily, via wireless communication - as used in this application, the term "operably coupled" includes wired and wireless data communication, whether direct or indirect via one or more intervening devices or components, and such data communication may be continuous or intermittent), and wearable device 102 transmits data regarding the defecation event to remote device 104. Remote device 104 can analyze, display, or otherwise provide data regarding the defecation event to one or more users, such as a clinical trial administrator, a healthcare provider, or the target himself or herself. These aspects are described in more detail below.

[0061] Continuing to refer to FIG. 1, the wearable device 102 includes a base 106 configured to be attached to a subject or otherwise carried by the subject. The base 106 carries an electronic device assembly 108 that facilitates detecting and recording the subject's defecation events. In the illustrated embodiment, the electronic device assembly 108 includes one or more wake-up sensors 110 that detect a “wake-up” stimulus preceding a defecation event. The wake-up sensors 110 are operatively coupled to a processor 112 (exemplarily, via wired communication). When the wake-up sensors 110 detect one or more wake-up stimuli, the processor 112 reconfigures the device 102 from a sleep mode to an active mode. In the sleep mode, one or more components of the electronic device assembly 108 may operate in a low power consumption state (e.g., operate at a lower clock rate, a lower voltage, or both), or may be inactive or “off”. In some embodiments, in the sleep mode, the processor 112 may be in a state where signals output from the sensors are ignored, not processed, or not stored. In the active mode, one or more components of the electronic device assembly 108 may operate in a high power consumption state (e.g., operate at a higher clock rate, a higher voltage, or both), or may be active or “on”. Exemplarily, such components may include one or more defecation event sensors 114 that detect a stimulus during a defecation event. In some embodiments, in the active mode, the processor 112 may be configured such that signals output from the sensors are processed and / or stored. Stated another way, the wake-up sensors 110 may wake up the device 102 for a potential defecation event, and the defecation event sensors 114 may then be used to confirm the occurrence of the defecation event.

[0062] The wake-up sensor 110 can take various forms. For example, the wake-up sensor 110 may include one or more optical sensors and / or one or more resistance sensors that detect when the subject removes lower body clothing. More specifically, the optical sensor detects increased light when the subject removes lower body clothing, and the resistance sensor detects the absence of the contact force applied by the lower body clothing. As another example, the wake-up sensor 110 may include one or more audio sensors that detect one or more corresponding sounds when the subject removes lower body clothing. As yet another example, the wake-up sensor 110 may include, for example, one or more accelerometers and / or an inertial measurement unit (IMU) that detect changes in the user's posture associated with sitting or standing. The wake-up stimulus detected by the wake-up sensor and causing the electronic assembly 108 to switch from an inactive state to an active state may include a single detected stimulus, such as increased light, removal of the contact force from the resistance sensor, and / or a sound associated with removing clothing. Alternatively or additionally, the wake-up stimulus may include a plurality of stimuli (e.g., any of the aforementioned stimuli) that are detected in a particular sequence or grouped in temporal proximity. Specific embodiments of wearable devices including such wake-up sensors are described in more detail below.

[0063] Similarly, the defecation event sensor 114 can take various forms. For example, the defecation event sensor 114 may include one or more electromyogram (EMG) electrodes for detecting the abdominal muscle electrical signals of the subject, which can indicate the contraction of those muscles during a defecation event, for example, during the Valsalva maneuver. As another example, the defecation event sensor 114 may include one or more electrocardiogram (ECG) electrodes and / or one or more photoplethysmography (PPG) sensors for detecting the cardiac electrical signals of the subject, which can indicate the decrease and subsequent increase in heart rate during the Valsalva maneuver. As another example, the defecation event sensor 114 may include one or more myogram (MMG) sensors for detecting the low-frequency vibrations of the abdominal muscles of the subject, which can indicate the contraction of those muscles during a defecation event. As another example, the defecation event sensor 114 may include one or more inertial measurement units (IMUs) for detecting changes in the posture of the subject, more specifically, changes to the sitting posture before a defecation event and / or changes to the standing posture after a defecation event. As another example, the defecation event sensor 114 may include one or more audio sensors for detecting sounds emitted from the subject's intestine, toilet flushing sounds, and / or sounds related to flatulence. As another example, the defecation event sensor 114 may include one or more gas sensors configured to detect flatulence. As another example, the defecation event sensor 114 may include one or more temperature sensors for detecting temperature changes in the intestine that may occur before or during a defecation event. As another example, the defecation event sensor 114 may include one or more optical sensors and / or one or more resistance sensors for detecting when the subject removes lower body clothing. Specific embodiments of the wearable device including such a defecation event sensor are described in more detail below.

[0064] Continuing to refer to FIG. 1, in the illustrated embodiment, the electronic device assembly 108 further includes one or more health sensors 115 that detect stimuli associated with the health of the subject. The health sensors 115 can take various forms. For example, the health sensor 115 may be a blood sensor that detects blood in the feces of the subject that may indicate one or more gastrointestinal diseases. More specifically, the blood sensor may be a solid vapor detection sensor that detects one or more volatile organic compounds (VOCs) that produce a "metallic odor" (e.g., hexanal, heptanal, octanal, nonanal, decanal, and / or 1-octen-3-one formed by the reaction of ions in the blood with lipids in the feces). In some embodiments, the health sensor 115 may typically be in a sleep mode, and the health sensor 115 may be reconfigured to an active mode when one or more of the defecation event sensors 114 detect a defecation event. Alternatively, the health sensor 115 may typically be in a sleep mode, and the health sensor 115 may be reconfigured to an active mode when one or more of the wake-up sensors 110 detect one or more wake-up stimuli.

[0065] Referring further to FIG. 1, the processor 112 can be any device or component capable of executing stored software and / or firmware code that, when executed by the processor 112, causes the wearable device 102 to perform the functions described herein. The processor 112 can be, for example, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), hardwired logic, combinations thereof, and the like.

[0066] Processor 112 is operably coupled (exemplarily, via wired communication) to a memory 116 for storing data regarding defect events and / or the health of the subject. Such data may include, for example, defecation event times, event lengths, wake-up sensor 110 and / or defecation event sensor 114 that detected the event, wake-up sensor 110 and / or defecation event sensor 114 that did not detect the event, data received from health sensor 115, and the like. Memory 116 can be any suitable computer-readable medium accessible by processor 112. Memory 116 may be a single storage device or multiple storage devices, may be located internal or external to processor 112, and may include both volatile and non-volatile media. Memory 116 may be, for example, random-access memory (RAM), read-only memory (ROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic storage devices, optical disk storage devices, or any other suitable medium capable of storing data and accessible by processor 112.

[0067] Processor 112 is also operably coupled (exemplarily, via wired communication) to a power source 118 to provide power to various components of the electronic device assembly 108, including wake-up sensor 110, defecation event sensor 114, and health sensor 115. Power source 118 can be, for example, one or more rechargeable batteries, one or more inductive / radio power receivers, and the like.

[0068] Continuing to refer to FIG. 1, the processor 112 is also operably coupled to the user interface 122 (exemplarily, via wired communication). The user interface 122 is operative to receive one or more user inputs (e.g., to manually confirm the occurrence of a defecation event), and / or to display data, information, and / or prompts generated by the system 100. The user interface 122 may include at least one input device for receiving user inputs. The user interface 122 may include a graphical user interface (GUI) having a touch screen display operative to display data and receive user inputs. Alternatively, the user interface 122 may include a non-touch screen display, a keyboard, a keypad, a microphone, a speaker, combinations thereof, and the like.

[0069] The processor 112 is further operably coupled (exemplarily, via wired communication) to a transmitter 120 for wirelessly transmitting information such as defecation event information and / or the subject's health data stored by the memory 116 to the remote device 104. The transmitter 120 can be, for example, a Bluetooth® transmitter, an IEEE 802.11 transmitter, a cellular communication transmitter, a short-range communication transmitter, and the like. The transmitter 120 can be continuously or intermittently coupled to the remote device 104. A transceiver (not shown) may be used instead of the transmitter 120 to facilitate the provision of information from the remote device 104 to the wearable device 102. Such information may include, for example, software updates.

[0070] The remote device 104 can be, for example, a mobile device such as a smartphone, smartwatch, or tablet device, a personal computer, a remote computer or database. In a clinical trial setting, the remote device 104 can analyze defecation event data and / or subject health data received from various wearable devices 102 and evaluate the effectiveness of one or more treatments provided to the subject using the wearable device 102. In other settings, the remote device 104 may include or be operably coupled to one or more displays for providing defecation event data and / or subject health data to a user such as a healthcare provider or subject using the wearable device 102.

[0071] System 100, and more specifically, wearable device 102, can be modified in various ways. For example, transmitter 120 may be coupled to remote device 104 via wired communication, or processor 112 may be operably coupled to one or more of the other components of electronic device assembly 108 via wireless communication. Similarly, in some embodiments, wearable device 102 may lack processor 112, and sensors 110, 114, 115 may instead be operably coupled to a processor of remote device 104, such as a processor of a smartphone. In some embodiments, wearable device 102 may not include user interface 122 and may instead rely on wireless communication with remote device 104 to communicate information to and / or receive commands from the user. As a further example, in some embodiments, there may be two or more wearable devices 102, each operably coupled to remote device 104. For example, there may be a first wearable device 102 in the form of a smartwatch and a second wearable device 102 in the form of a patch configured to be carried on the body of the subject, and both wearable devices are operably coupled to remote device 104 (e.g., a smartphone). In such embodiments, all wearable devices may include similar sensors 110, 114, and / or 115, or the wearable devices may include different sensors 110, 114, and / or 115. As a specific example, a first wearable device 102 in the form of a smartwatch may include a voice sensor and an accelerometer / movement sensor that function as defecation event sensors, while a second wearable device 102 in the form of a body patch may include other types of defecation event sensors (e.g., a gas sensor, a myograph sensor, and / or an electromyogram sensor). Since all wearable devices are operably coupled to remote device 104, remote device 104 can use the inputs from all operably coupled wearable devices to detect defecation events.

[0072] Referring now to FIGS. 2 and 3, a wearable device 202 according to an embodiment of the present disclosure is shown. The wearable device 202 is a more specific embodiment of the wearable device 102 described above. Thus, the wearable device 202 includes some of the same components and operates in the same manner as the wearable device 102 described above. The wearable device 202 is also operatively coupled to one or more remote devices 104 as described above. The wearable device 202 includes a base or housing 206 configured to be removably worn by a subject. More specifically, the base 206 is a patch that can be adhesively fixed to the subject's torso. The patient-facing side 222 of the base 206 can be adhesively fixed to the subject. The patient-facing side 222 of the base 206 also includes one or more defecation event sensors 214, more specifically, a plurality of electromyogram electrodes 224 for detecting the subject's abdominal muscle electrical signals. The base 206 also carries one or more wake-up sensors 210, more specifically, an elongated optical sensor 226 disposed under lower body clothing and configured to detect when the subject removes the lower body clothing. The base 206 carries a processor 212 and a power source 218 internally. In some embodiments, the base 206 can carry additional sensors such as an inertial measurement unit, a gas sensor, a myogram sensor, or any other sensor contemplated herein.

[0073] Referring to FIGS. 4A and 4B, a wearable device 302 according to another embodiment of the present disclosure is shown. The wearable device 302 is another more specific embodiment of the wearable device 102 described above. Thus, the wearable device 302 includes some of the same components and operates in the same manner as the wearable device 102 described above. The wearable device 302 is also operatively coupled to one or more remote devices 104 as described above. The wearable device 302 includes a base 306 configured to be removably worn by a subject. More specifically, the base 306 is a patch that can be adhesively fixed to the subject's torso. The patient-facing side 322 of the base 306 can be adhesively fixed to the subject. The patient-facing side 322 of the base 306 also includes one or more defecation event sensors 314, more specifically, a plurality of electromyogram electrodes 324 for detecting the subject's abdominal muscle electrical signals, and a myophonogram sensor 325 (FIG. 4B) for detecting the subject's abdominal muscle movement signals. The opposite side 328 of the base 306 removably carries a hub 330 (FIG. 4A) that is operatively coupled to the defecation event sensor 314. The hub 330 carries a processor (not shown), a memory (not shown), and a power source (not shown). The hub 330 may be removed from the base 306 to facilitate recharging of the power source. The hub 330 also carries a user interface 320 (FIG. 4A), more specifically, one or more lights 332 and a speaker 334 for providing information to the subject. In some embodiments, the base 306 may carry additional sensors such as an optical sensor, an audio sensor, an inertial measurement unit, a gas sensor, or any of the other sensors contemplated herein.

[0074] Referring to FIG. 5, a wearable device 402 according to yet another embodiment of the present disclosure is shown. Wearable device 402 is yet another more specific embodiment of wearable device 102 described above. Thus, wearable device 402 includes some of the same components and operates in the same manner as wearable device 102 described above. Wearable device 402 is also operatively coupled to one or more remote devices 104 as described above. Wearable device 402 includes a base 406 configured to be removably worn by a subject. More specifically, base 406 is a belt that can extend around the subject's torso and can be secured to the torso. The patient-facing side 422 of base 406 also includes one or more defecation event sensors 414, more specifically, a plurality of electromyogram electrodes 424, an inertial measurement unit 436, and a myophonogram sensor 438. In some embodiments, base 406 may carry additional sensors such as an optical sensor, a voice sensor, or any of the other sensors contemplated herein.

[0075] Referring now to FIGS. 6-8, a wearable device 502 according to yet another embodiment of the present disclosure is shown. Wearable device 502 is yet another more specific embodiment of wearable device 102 described above. Accordingly, wearable device 502 includes some of the same components and operates in the same manner as wearable device 102 described above. Wearable device 502 is also operatively coupled to one or more remote devices 104 as described above. Wearable device 502 includes a base or housing 506 configured to be removably worn by a subject. More specifically, base 506 carries a patch 540 (FIG. 6) that can be adhesively fixed to the subject's torso. Base 506 also carries one or more defecation event sensors 514 (FIG. 8). More specifically, base 506 carries an electromyogram sensor 524 for detecting the subject's abdominal muscle movement signals and a temperature sensor 542 for detecting changes in the temperature of the intestine. Base 506 carries a processor 512 (FIG. 8) and a battery (not shown) internally. Base 506 further carries a user interface 520 (FIG. 7). User interface 520 includes a first input 544 operable to reconfigure device 502 from a sleep mode to an active mode. User interface 520 further includes a second input 546 operable to reconfigure device 502 from an active mode to a sleep mode. In some embodiments, base 506 may carry additional sensors such as an optical sensor, an inertial measurement unit, an audio sensor, a gas sensor, an electromyogram sensor, or any of the other sensors contemplated herein.

[0076] Systems according to embodiments of the present disclosure can determine the occurrence of defecation events in various ways. For example, in some embodiments, the system can determine the occurrence of a defecation event when at least a specific number of defecation event sensors 114 detect a corresponding stimulus (more specifically, when most of the defecation event sensors 114 detect a corresponding stimulus). In other embodiments, the system can determine the occurrence of a defecation event when all of the defecation event sensors 114 detect a corresponding stimulus. In some embodiments, the system can use machine learning to improve the accuracy of determining the occurrence of a defecation event for a specific subject. More specifically, some systems can recognize that one or more specific stimuli are consistently detected before or during a defecation event of a specific subject, and the system can weight more heavily the information received from the corresponding sensors when determining the occurrence of a defecation event. In some embodiments, the system can determine the occurrence of a defecation event when the defecation event sensors 114 detect a corresponding stimulus according to a specific sequence or algorithm. Similarly, in some embodiments, when the wake-up sensor 110 detects a corresponding stimulus according to a specific sequence or algorithm, the system can reconfigure the device from sleep mode to active mode.

[0077] Referring to FIG. 9, a method 600 for detecting a defecation event of a subject according to an embodiment of the present disclosure is shown. Before starting method 600, a wearable device, such as any of the wearable devices contemplated herein, is attached to the subject's torso. Method 600 begins at block 602 by detecting, by an optical sensor of the wearable device, increased light when the subject removes lower body clothing. Method 600 continues at block 604 by detecting, by an electromyogram sensor of the wearable device, an abdominal muscle movement signal of the subject. Method 600 ends at block 606 by determining the occurrence of a defecation event based at least in part on the detected abdominal muscle movement signal of the subject and the increased light detected when the subject removes lower body clothing.

[0078] Method 600 may be modified in various ways and / or may include various additional operations. For example, method 600 may further include reconfiguring the wearable device from a sleep mode in which the electromyogram sensor is inactive to an active mode in which the electromyogram sensor is configured to detect the subject's rectus abdominis movement signal based on increased light detected when the subject removes lower body clothing. As another example, method 600 may further include detecting flatulence by a gas sensor of the wearable device, and the determination that a defecation event has occurred may be based at least in part on the detected flatulence. As yet another example, method 600 may further include detecting the subject's rising movement after detecting flatulence by an inertial measurement unit of the wearable device, and the determination that a defecation event has occurred may be based at least in part on the detected rising movement. As yet another example, method 600 may further include detecting the subject's sitting movement by an inertial measurement unit of the wearable device before detecting the subject's rectus abdominis movement signal, and the determination that a defecation event has occurred may be based at least in part on the detected sitting movement. As another example, method 600 may be repeated over a period of time, such as multiple days, weeks, months, or years, to determine multiple defecation events. As an alternative example, the detection of the rectus abdominis movement signal in block 604 may precede the detection of the increased light in block 602.

[0079] Figures 10 to 16 show operations associated with another method for detecting a target defecation event according to an embodiment of the present disclosure. Before starting the method, a wearable device 702, such as any of the wearable devices contemplated herein, is attached to the torso of the target S. As shown in Figure 10, the method starts by (A) detecting the abdominal muscle movement signal of the target S by a myogram sensor (not shown) of the wearable device 702, (B) detecting the temperature change of the target's intestine by a temperature sensor (not shown) of the wearable device 702, and / or (C) detecting the sound emitted from the target's intestine by a voice sensor (not shown) of the wearable device 702. As shown in Figure 11, the method continues by detecting the increased light when the target S removes the lower body clothing by an optical sensor (not shown) of the wearable device 702. As shown in Figure 12, the method then includes detecting the sitting movement of the target S by an inertial measurement unit (not shown) of the wearable device 702. As shown in Figure 13, the method continues by detecting the abdominal muscle movement signal of the target S by a myogram sensor (not shown) of the wearable device 702. As shown in Figure 14, the method then includes detecting flatulence by a gas sensor (not shown) of the wearable device 702. As shown in Figure 15, the method continues by detecting the standing movement of the target S by an inertial measurement unit (not shown) of the wearable device 702. As shown in Figure 16, the method then includes (A) detecting the hand movement for flushing the toilet by an inertial measurement unit (not shown) of a remote device, exemplarily a smartwatch 704 worn by the target S, and / or (B) detecting the toilet flushing sound by a voice sensor (not shown) of the remote device. The method ends by determining the occurrence of a defecation event based at least in part on one or more of the previously detected stimuli.The method for detecting defecation events presented in FIGS. 10 to 16 may be modified by omitting one or more of the previously presented steps, adding additional steps between some of the previously presented steps, and / or rearranging the order of the presented steps. For example, in some embodiments, the method may not include detecting intestinal temperature changes and / or sounds emitted from the subject's intestine (as shown in FIG. 10), but instead may start with detecting increased light when the subject S removes lower body clothing (as shown in FIG. 11). In some embodiments, the method may omit detecting flatulence (as shown in FIG. 14). In still other embodiments, the method may omit detecting changes in posture by an inertial measurement unit, as shown in FIGS. 13 and 15. Any sequence of all or any subset of the presented steps can be used to determine the occurrence of a defecation event.

[0080] The sensitivity and specificity of the systems and methods described herein for detecting a target bowel movement event can be improved using machine learning techniques. Referring to FIG. 17, a method 800 for training one or more processors to detect a bowel movement event, according to an embodiment of the present disclosure, is shown. Before starting method 800, a first wearable device, such as a smartwatch or any of the wearable devices contemplated herein, and a second wearable device, such as any of the wearable devices contemplated herein, can be provided to a subject for attachment to the subject's body. Both wearable devices may take the form of wearable device 102 described herein (see, e.g., FIG. 1). The first wearable device may include a first bowel movement sensor 114 configured to detect one or more first stimuli, and the second wearable device may include a second bowel movement sensor 114 configured to detect one or more second stimuli. The first wearable device and the second wearable device are operably coupled to one or more processors in any of the manners contemplated herein, either wirelessly or through a wired connection. In some embodiments, the one or more processors may include a first processor operably coupled to the first wearable device (e.g., embedded within or in wireless communication with the first wearable device) and a second processor operably coupled to the second wearable device (e.g., embedded within or in wireless communication with the second wearable device). In such embodiments, the first processor may be operably coupled to the second processor such that the two processors can function together to implement method 800. In some embodiments, the one or more processors may consist of a single processor operably coupled to both the first wearable device and the second wearable device. This single processor may be present on the first wearable device, on the second wearable device, or on a remote device 104 (e.g., a smartphone) operably coupled to both wearable devices.

[0081] Method 800 begins, at block 802, by detecting, by one or more first defecation event sensors carried by a first wearable device, one or more first defecation stimuli. Method 800 continues, at block 804, by detecting, by one or more second defecation event sensors carried by a second wearable device, one or more second stimuli associated with one or more defective events of a subject. The stimuli detected (by both the first defecation event sensors and the second defecation event sensors) can be any of the stimuli contemplated herein. In some embodiments, the first defecation stimulus and the second defecation stimulus can be different types of stimuli. For example, the second defecation event sensor can be an EMG electrode and / or an MMG sensor, the second stimulus can be the subject's abdominal muscle electrical signal and / or abdominal muscle movement signal, the first defecation event sensor can be an inertial measurement unit, and the first stimulus can be the subject's movement. In other embodiments, the first defecation stimulus and the second defecation stimulus can be the same type of stimulus. Method 800 proceeds to block 806 by determining, by one or more processors, based on the second stimulus, the occurrence of the detected defecation event of the subject. This determination can be achieved through any of the methods discussed herein. Method 800 continues, at block 808, by associating, by one or more processors, the first stimulus detected by the first defecation event sensor within a predetermined period of the detected defecation event (e.g., within 60, 120, 180, and / or 240 seconds before and / or after the detected defecation event) with the defecation event of the subject. The one or more processors can then train a machine learning algorithm for detecting the defecation event of the subject using the data indicative of the first stimulus detected by the first defecation event sensor associated with the defecation event of the subject.

[0082] Method 800 may be useful for training a machine learning algorithm implemented on one or more processors to detect a subject's defecation event using a first stimulus detected by a first defecation event sensor. In particular, method 800 may be useful in situations where a second defecation event sensor can initially detect defecation events with higher sensitivity and / or specificity than the first defecation event sensor, but ultimately it is desirable to detect defecation events using only the first defecation event sensor, or primarily using the first defecation event sensor.

[0083] By way of illustrative and non-limiting example, the second wearable device can be a patch configured to be affixed to the subject's torso, as described herein. The patch can include an MMG sensor, an EMG sensor, an ECG sensor, an optical sensor, a gas sensor, and / or other sensors as described herein. The first wearable device can be a smartwatch configured to be worn on the subject's arm. The smartwatch can include additional or different sensors such as an accelerometer / inertial measurement unit (IMU) and / or a voice sensor. The second wearable device may initially be capable of detecting defecation events with higher specificity and sensitivity than the first wearable device, but wearing the device by the subject over a long period of time may be more cumbersome and / or inconvenient. Thus, it may be desirable to ultimately train one or more processors to use only the first wearable device (i.e., the smartwatch) to detect defecation events without the assistance of the second wearable device. To achieve this training, when the second wearable device detects a defecation event using the techniques described herein, it may instruct one or more processors to associate the motion and / or voice signals recently recorded by the smartwatch (e.g., within 60 or 120 seconds before or after the detected defecation event) with the defecation. In this way, the second wearable device provides a ground truth label that enables the smartwatch to distinguish motion and / or voice signals associated with defecation from those not associated with defecation. Over time, as the wearable patch trains one or more processors, the processors can use the stimuli detected by the smartwatch to predict and / or detect defecation with higher accuracy. Ultimately, when the smartwatch (or mobile device) is fully trained, the subject may stop wearing the wearable patch and rely only on the smartwatch to detect defecation events.

[0084] Training of a machine learning algorithm in one or more processors can be achieved using any known machine learning technique. For example, one or more processors may use a neural network having multiple layers of nodes to predict or detect a defect event based on stimuli detected by a first defecation event sensor. The sensitivity and specificity of such a neural network can be improved by using ground truth data that provides examples of first stimuli associated with defecation events and examples of first stimuli not associated with defecation events to adjust the weights associated with such layers of nodes. Such ground truth data can be provided by a second defecation event sensor on a second wearable device as described herein. The weights within the neural network can be adjusted using an iterative training procedure that compares the output predicted based on a particular first stimulus to the ground truth label provided by a second wearable device indicating whether such first stimulus is associated with a defecation event. If the prediction of the neural network does not match the ground truth label, the weights can be adjusted according to a loss function to improve the match between the network's prediction and the ground truth label. In this way, by providing a ground truth label indicating whether a particular first stimulus is associated with a defecation event, the weights of the neural network can be adjusted to improve the network's detection of defecation events based on the first stimulus alone.

[0085] Method 800 may be modified in various ways and / or may include various additional operations. For example, one or more processors may be trained to determine the occurrence of a defecation event when one or more defecation event sensors of a first wearable device detect corresponding stimuli according to a particular sequence or algorithm. As another example, the first wearable device may further include a wake-up sensor configured to detect one or more wake-up stimuli. The first wearable device may be further configured to transition from a sleep mode to an active mode in which the first wearable device is configured to detect a defecation event of a subject via the first defecation event sensor when the wake-up sensor detects a wake-up stimulus associated with the defecation event of the subject. Method 800 may be modified by causing one or more processors to associate a wake-up stimulus detected by the wake-up sensor within a second predetermined period of the detected defecation event (e.g., within 60, 120, 180, and / or 240 seconds before and / or after the detected defecation event) with the defecation event. The second predetermined period may be the same as or different from the predetermined period. In this way, one or more processors can be trained not only to detect defecation events with higher sensitivity and specificity but also to wake up from the sleep mode to the active mode to detect such defecation events with higher accuracy. This can help save power used by one or more processors and / or the first wearable device.

[0086] Referring to FIG. 18, another method 900 for training one or more processors to detect defecation events according to an embodiment of the present disclosure is shown. Before starting method 900, a wearable device, such as any of the wearable devices contemplated herein, may be provided to the subject for attachment to the subject's body. The wearable device may take the form of the wearable device 102 described herein (see, e.g., FIG. 1) and may include a defecation event sensor 114 configured to detect one or more stimuli. The wearable device and the defecation event sensor provided therein may be operably coupled to one or more processors. The one or more processors may be provided on the wearable device or may communicate wirelessly with the wearable device. For example, the one or more processors may be present on a remote device 104 (e.g., a smartphone).

[0087] Method 900 begins at block 902 by detecting, by the defecation event sensor of the wearable device, one or more stimuli. Method 900 continues to block 904 by receiving, via the subject's mobile device (e.g., remote device 104), a user input indicating the time of defecation at which the defecation event occurred. The user input may include a manual input from the subject (e.g., actuation of a physical button and / or a virtual button on a touch screen, voice input) that the defecation event occurred at the time the subject provided the input. Alternatively, or in addition, the user input may include a manual input from the subject indicating that the defecation event occurred at a specific past time. Alternatively, or in addition, the user input may include a manual input from the subject indicating that a defecation event is about to occur (e.g., the subject is about to defecate).

[0088] Method 900 continues at block 906 by associating, by one or more processors, a stimulus detected by a defecation event sensor within a predetermined period of the defecation time received from a subject (e.g., within 60, 120, 180, and / or 240 seconds before and / or after the defecation time) with the defecation event. The one or more processors can then train a machine learning algorithm for detecting a defecation event of the subject using data indicative of a first stimulus detected by a first defecation event sensor associated with the defecation event of the subject. In this way, the machine learning algorithm implemented by the one or more processors for detecting a defecation event based on the stimulus detected by the defecation event sensor can be trained using ground truth data manually provided by the subject. Such training can be implemented using any of the techniques described herein.

[0089] The present invention has been shown and described with reference to the preferred embodiments, however, the present invention may be modified within the spirit and scope of the present disclosure. Accordingly, the present application is intended to cover any variations, uses, or adaptations of the present invention using its general principles. Further, the present application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which the present invention pertains.

[0090] Various aspects are described in the present disclosure, including but not limited to the following aspects. Aspect 1. A system for detecting a target defecation event, the system comprising a wearable device configured to be carried on the body of the target, the wearable device being operable in a sleep mode and an active mode, the wearable device comprising a wake-up sensor configured to detect a first stimulus, a myogram sensor configured to detect an abdominal muscle movement signal of the target, and a wearable device, a processor operably coupled to the wake-up sensor and the myogram sensor, the processor being configured to switch the wearable device from the sleep mode to the active mode based on the first stimulus detected by the wake-up sensor, and in the active mode, the wearable device communicates with the processor and is configured to determine the occurrence of a defecation event of the target based on the abdominal muscle movement signal detected by the myogram sensor. A system comprising a processor. Aspect 2. The abdominal muscle movement signal of the target is a second stimulus, and the system further comprises a third sensor operably coupled to the processor and configured to detect a third stimulus. In the active mode, the processor is configured to determine the occurrence of a defecation event of the target based on the abdominal muscle movement signal detected by the myogram sensor and the third stimulus detected by the third sensor. The system according to Aspect 1. Aspect 3. The system according to Aspect 2, wherein the third sensor is disposed within the wearable device and includes a gas sensor configured to detect flatulence. Aspect 4. The system according to Aspect 2, wherein the third sensor is an audio sensor configured to detect the sound of toilet flushing. Aspect 5. The system according to Aspect 2, wherein the third sensor is an electromyogram electrode configured to detect an electromyogram signal of the target. Aspect 6. The system according to aspect 2, wherein the third sensor is an inertial measurement unit configured to detect a change in the posture of the object. Aspect 7. The system according to any one of aspects 1 to 6, wherein the wearable device further comprises a patch configured to be carried on the trunk of the object, and the patch carries the wake-up sensor and the electromyogram sensor. Aspect 8. The system according to aspect 7, wherein the patch further carries the processor. Aspect 9. The system according to any one of aspects 1 to 6, wherein the wearable device further comprises a belt configured to extend around the trunk of the object, and the belt carries the wake-up sensor and the electromyogram sensor. Aspect 10. The system according to aspect 9, wherein the belt further carries the processor. Aspect 11. The system according to any one of aspects 1 to 10, wherein the wake-up sensor includes one of an optical sensor and a resistance sensor configured to detect when the object takes off lower body clothing. Aspect 12. The system according to any one of aspects 1 to 11, wherein the wearable device further comprises a health sensor configured to detect a health stimulus associated with the health of the object. Aspect 13. The system according to aspect 12, wherein the health sensor includes a blood sensor configured to detect blood in the feces of the object. Aspect 14. The system according to aspect 13, wherein the blood sensor includes a solid vapor detection sensor configured to detect one or more volatile organic compounds. Aspect 15. A system for detecting a defecation event of an object, the system comprising: A wearable device configured to be carried on the trunk of the object, the wearable device comprising: An electromyogram sensor configured to detect an abdominal muscle movement signal of the object; and A gas sensor configured to detect flatulence; and a wearable device; A processor operably coupled to the electromyogram sensor and the gas sensor, the processor being configured to determine the occurrence of a defecation event of the object based on the abdominal muscle movement signal detected by the electromyogram sensor and the flatulence detected by the gas sensor. A system comprising a processor. Aspect 16. The system according to aspect 15, wherein the processor is configured to determine the occurrence of a defecation event of the subject based on a series of events including that one of the abdominal muscle movement signal detected by the myogram sensor and the flatulence detected by the gas sensor precedes the other of the abdominal muscle movement signal detected by the myogram sensor and the flatulence detected by the gas sensor. Aspect 17. The system according to aspect 15 or 16, wherein the wearable device further comprises a base carrying the myogram sensor, the gas sensor, and the processor. Aspect 18. A system for detecting a defecation event of a subject, the system comprising: A wearable device configured to be carried on the body of the subject and under lower body clothing worn by the subject, the wearable device comprising: An optical sensor configured to detect increased light when the subject removes the lower body clothing, and A processor operably coupled to the optical sensor, the processor being configured to determine the occurrence of a defecation event based at least in part on the increased light detected by the optical sensor. Aspect 19. The system according to aspect 18, wherein the wearable device is operable in an active mode and a sleep mode, and the processor is configured to switch the wearable device from the sleep mode to the active mode when it determines that the subject has removed the lower body clothing in response to the increased light detected by the optical sensor. Aspect 20. The system according to aspect 19, wherein the optical sensor is a first sensor configured to detect light as a first stimulus, the wearable device further comprises a second sensor configured to detect a second stimulus when the wearable device is in the active mode, the second stimulus being different from the first stimulus, the processor is operably coupled to the second sensor, and the processor is configured to determine the occurrence of a defecation event of the subject based on a signal received from the second sensor. Aspect 21. The optical sensor is a first sensor configured to detect light as a first stimulus, the wearable device further comprises a second sensor configured to detect a second stimulus when the wearable device is in the active mode, the second stimulus is different from the first stimulus, the processor is operably coupled to the second sensor, and the processor is configured to determine the occurrence of a defecation event of the subject based on signals received from the first sensor and the second sensor. The system according to aspect 19. Aspect 22. The system according to aspect 20 or 21, wherein the second sensor is an electromyogram sensor. Aspect 23. The wearable device further comprises a patch configured to be carried on the body of the subject, and the patch carries the optical sensor and the processor. The system according to any one of aspects 18 to 22. Aspect 24. The wearable device further comprises a belt configured to extend around the body of the subject, and the belt carries the optical sensor and the processor. The system according to any one of aspects 18 to 22. Aspect 25. A method for detecting a defecation event of a subject, the method comprising: detecting, by an optical sensor of a wearable device carried on the body of the subject, increased light when the subject removes lower body clothing; detecting, by an electromyogram sensor of the wearable device, an abdominal muscle movement signal of the subject; determining the occurrence of the defecation event based at least in part on the increased light detected when the subject removes the lower body clothing and the detected abdominal muscle movement signal of the subject. Aspect 26. The method according to aspect 25, further comprising detecting flatulence by a gas sensor of the wearable device, and the determination that the defecation event has occurred is based at least in part on the detected flatulence. Aspect 27. The method according to aspect 25 or 26, further comprising detecting, by an inertial measurement unit of the wearable device, a sitting movement by the subject before detecting the abdominal muscle movement signal of the subject, and the determination that the defecation event has occurred is based at least in part on the detected sitting movement. Aspect 28. The method according to any one of Aspects 25 to 27, further comprising detecting, by the inertial measurement unit of the wearable device, the movement of the subject standing up after detecting flatulence, and the determination that the defecation event has occurred is at least partially based on the detected movement of standing up. Aspect 29. The method according to any one of Aspects 25 to 28, further comprising detecting a plurality of defecation events of the subject over a certain period. Aspect 30. Detecting each of the plurality of defecation events of the subject comprises: detecting, by the myogram sensor of the wearable device, the abdominal muscle movement signal of the subject; detecting, by the optical sensor, the increased light when the subject takes off the lower body clothing; determining the occurrence of each of the plurality of defecation events based at least partially on the detected abdominal muscle movement signal of the subject and the detected increased light when the subject takes off the lower body clothing. The method according to Aspect 29. Aspect 31. The method according to any one of Aspects 25 to 30, wherein detecting the increased light by the optical sensor when the subject takes off the lower body clothing precedes detecting the abdominal muscle movement signal of the subject by the myogram sensor. Aspect 32. The method according to any one of Aspects 25 to 30, wherein detecting the abdominal muscle movement signal of the subject by the myogram sensor precedes detecting the increased light by the optical sensor when the subject takes off the lower body clothing. Aspect 33. The method according to any one of Aspects 25 to 30, further comprising reconfiguring the wearable device from the sleep mode to the active mode based on the detected increased light when the subject takes off the lower body clothing. In the sleep mode, the myogram sensor is inactive, and in the active mode, the myogram sensor is configured to detect the abdominal muscle movement signal of the subject. Aspect 34. A system for training one or more processors to detect defecation events of a subject, the system comprising: A first wearable device, wherein the first wearable device is configured to be carried on the body of the subject, and the first wearable device comprises a first defecation event sensor configured to detect one or more first stimuli. A second wearable device configured to be carried on the body of the subject, wherein the second wearable device comprises a second defecation event sensor configured to detect one or more second stimuli. A system comprising: one or more processors operably coupled to the first defecation event sensor and the second defecation event sensor, configured to determine the occurrence of a detected defecation event of the subject based on the one or more second stimuli, and to associate a first stimulus detected by the first defecation event sensor within a predetermined period of the detected defecation event with the defecation event of the subject. Aspect 35. The system according to aspect 34, wherein the one or more processors are further configured to train a machine learning algorithm for detecting a defecation event of the subject using data indicating a first stimulus detected by the first defecation event sensor associated with the defecation event of the subject. Aspect 36. The first wearable device further comprises a wake-up sensor configured to detect one or more wake-up stimuli. The first wearable device is configured to transition from a sleep mode to an active mode in which the first wearable device is configured to detect a defecation event of the subject via the first defecation event sensor when the wake-up sensor detects a wake-up stimulus associated with the defecation event of the subject. The system according to aspect 34 or 35, wherein the one or more processors are further configured to associate a wake-up stimulus detected by the wake-up sensor within a second predetermined period of the detected defecation event with the defecation event of the subject. Aspect 37. The one or more processors include a first processor operably coupled to the first defecation event sensor and a second processor operably coupled to the second defecation event sensor, and the first processor and the second processor are operably coupled to each other. The system according to any one of Aspects 34 to 36. Aspect 38. The one or more processors consist of a single processor operably coupled to the first defecation event sensor and the second defecation event sensor. The system according to any one of Aspects 34 to 36. Aspect 39. The first wearable device includes a smartwatch. The system according to any one of Aspects 34 to 38. Aspect 40. The second wearable device includes a patch configured to be carried on the subject's torso. The system according to any one of Aspects 34 to 39. Aspect 41. The first wearable device includes at least one of the one or more processors. The system according to any one of Aspects 34 to 40. Aspect 42. At least one of the one or more processors communicates wirelessly with the first wearable device. The system according to any one of Aspects 34 to 40. Aspect 43. The first stimulus and the second stimulus are different types of stimuli. The system according to any one of Aspects 34 to 42. Aspect 44. The first stimulus and the second stimulus are the same type of stimulus. The system according to any one of Aspects 34 to 42. Aspect 45. A method for training one or more processors operably coupled to a first wearable device to detect a subject's defecation event, the method comprising: detecting, by a first defecation event sensor carried by the first wearable device, one or more first stimuli; detecting, by a second defecation event sensor carried by a second wearable device, one or more second stimuli; determining, by the one or more processors, the occurrence of the detected defecation event of the subject based on the second stimulus; associating, by the one or more processors, a first stimulus detected by the first defecation event sensor within a predetermined period of the detected defecation event with the subject's defecation event. Aspect 46. The method according to aspect 45, further comprising training, by the one or more processors, a machine learning algorithm for detecting a defecation event of the subject using data indicating a first stimulus detected by the first defecation event sensor associated with the defecation event of the subject. Aspect 47. The first wearable device further comprises a wake-up sensor configured to detect one or more wake-up stimuli, and the first wearable device is configured to transition from a sleep mode to an active mode in which the first wearable device is configured to detect a defecation event of the subject via the first defecation event sensor when the wake-up sensor detects a wake-up stimulus associated with the defecation event of the subject, and the method The method according to aspect 45, further comprising associating, by the one or more processors, a wake-up stimulus detected by the wake-up sensor within a second predetermined period of the detected defecation event with the defecation event of the subject. Aspect 48. The one or more processors comprise a first processor operably coupled to the first defecation event sensor and a second processor operably coupled to the second defecation event sensor, and the first processor and the second processor are operably coupled to each other. The method according to any one of aspects 45 to 47. Aspect 49. The one or more processors consist of a single processor operably coupled to the first defecation event sensor and the second defecation event sensor. The method according to any one of aspects 45 to 48. Aspect 50. The first wearable device includes a smartwatch. The method according to any one of aspects 45 to 48. Aspect 51. The second wearable device comprises a patch configured to be carried on the body of the subject. The method according to any one of aspects 45 to 50. Aspect 52. The first wearable device comprises at least one of the one or more processors. The method according to any one of aspects 45 to 51. Aspect 53. At least one of the one or more processors communicates wirelessly with the first wearable device. The method according to any one of aspects 45 to 51. Aspect 54. The method according to any one of aspects 45 to 52, wherein the first stimulus and the second stimulus are different types of stimuli. Aspect 55. The method according to any one of aspects 45 to 52, wherein the first stimulus and the second stimulus are the same type of stimuli. Aspect 56. A system for training one or more processors to detect a defecation event of a subject, the system comprising: A wearable device configured to be carried on the body of the subject, the wearable device comprising a defecation event sensor configured to detect one or more stimuli; A mobile device configured to receive user input from the subject indicating a defecation time at which a defecation event occurred; One or more processors operably coupled to the defecation event sensor and the mobile device and configured to associate a stimulus detected by the defecation event sensor within a predetermined period of the defecation time with the defecation event of the subject. Aspect 57. The system according to aspect 56, wherein the one or more processors are further configured to train a machine learning algorithm for detecting the defecation event of the subject using data indicating a first stimulus detected by the defecation event sensor associated with the defecation event of the subject. Aspect 58. The wearable device further comprises a wake-up sensor configured to detect one or more wake-up stimuli, The wearable device is configured to transition from a sleep mode to an active mode in which the wearable device is configured to detect the defecation event of the subject via the defecation event sensor when the wake-up sensor detects a wake-up stimulus associated with the defecation event of the subject, The system according to aspect 56 or 57, wherein the one or more processors are further configured to associate a wake-up stimulus detected by the wake-up sensor within a second predetermined period of the detected defecation event with the defecation event of the subject. Aspect 59. The system according to any one of aspects 56 to 58, wherein the mobile device comprises at least one of the one or more processors. Aspect 60. The wearable device is the system according to any one of Aspects 56 to 59, comprising at least one of the one or more processors. Aspect 61. The wearable device is the system according to any one of Aspects 56 to 60, comprising at least one of a smartwatch and a patch configured to be carried on the body of the subject. Aspect 62. A method for training one or more processors operably coupled to a wearable device to detect a defecation event of a subject, the method comprising: detecting, by a defecation event sensor carried by the wearable device, one or more stimuli; receiving user input via the subject's mobile device indicating a defecation time at which a defecation event occurred; associating, by the one or more processors, the stimuli detected by the defecation event sensor within a predetermined period of the defecation time with the defecation event of the subject. Aspect 63. The method according to Aspect 62, further comprising training a machine learning algorithm for detecting a defecation event of the subject using data indicating a first stimulus detected by the defecation event sensor associated with the defecation event of the subject by the one or more processors. Aspect 64. The wearable device further comprises a wake-up sensor configured to detect one or more wake-up stimuli, and the wearable device is configured to transition from a sleep mode to an active mode when the wake-up sensor detects a wake-up stimulus associated with a defecation event of the subject, and the method comprises: The method according to Aspect 62 or 63, further comprising associating, by the one or more processors, the wake-up stimuli detected by the wake-up sensor within a second predetermined period of the detected defecation event with the defecation event of the subject. Aspect 65. The mobile device comprises at least one of the one or more processors, and is the method according to any one of Aspects 62 to 64. Aspect 66. The wearable device comprises at least one of the one or more processors, and is the method according to any one of Aspects 62 to 65. Aspect 67. The method according to any one of Aspects 62 to 66, wherein the wearable device includes at least one of a smartwatch and a patch configured to be carried on the body of the subject.

Claims

**Claim 1** A system for detecting a target defecation event, the system comprising: A wearable device configured to be carried on the body of the target, the wearable device being operable in a sleep mode and an active mode, the wearable device comprising: A wake-up sensor configured to detect a first stimulus; A myogram sensor configured to detect an abdominal muscle movement signal of the target; and a wearable device; A processor operably coupled to the wake-up sensor and the myogram sensor, the processor being configured to switch the wearable device from the sleep mode to the active mode based on the first stimulus detected by the wake-up sensor, and in the active mode, the wearable device is configured to communicate with the processor and determine the occurrence of a defecation event of the target based on the abdominal muscle movement signal detected by the myogram sensor. A system comprising a processor. **Claim 2** The abdominal muscle movement signal of the target is a second stimulus, and the system further comprises a third sensor operably coupled to the processor and configured to detect a third stimulus. In the active mode, the processor is configured to determine the occurrence of a defecation event of the target based on the abdominal muscle movement signal detected by the myogram sensor and the third stimulus detected by the third sensor. The system according to claim 1. **Claim 3** The system according to claim 2, wherein the third sensor is disposed within the wearable device and includes a gas sensor configured to detect flatulence. **Claim 4** The system according to claim 2, wherein the third sensor is an audio sensor configured to detect toilet flushing sound. **Claim 5** The system according to claim 2, wherein the third sensor is an electromyogram electrode configured to detect an electromyogram signal of the target's muscles. **Claim 6** The system according to claim 2, wherein the third sensor is an inertial measurement unit configured to detect a change in the posture of the target. **Claim 7** The wearable device further comprises a patch configured to be carried on the torso of the subject, and the patch carries the wake-up sensor and the electromyogram sensor. The system according to any one of claims 1 to 6.

8. The patch further carries the processor. The system according to claim 7.

9. The wearable device further comprises a belt configured to extend around the torso of the subject, and the belt carries the wake-up sensor and the electromyogram sensor. The system according to any one of claims 1 to 6.

10. The belt further carries the processor. The system according to claim 9.

11. The wake-up sensor includes one of an optical sensor and a resistance sensor configured to detect when the subject removes lower body clothing. The system according to any one of claims 1 to 6.

12. The wearable device further comprises a health sensor configured to detect a health stimulus associated with the health of the subject. The system according to any one of claims 1 to 6.

13. The health sensor includes a blood sensor configured to detect blood in the feces of the subject. The system according to claim 12.

14. The blood sensor includes a solid vapor detection sensor configured to detect one or more volatile organic compounds. The system according to claim 13.

15. A system for detecting a defecation event of a subject, the system comprising: A wearable device configured to be carried on the torso of the subject, the wearable device comprising: An electromyogram sensor configured to detect an abdominal muscle movement signal of the subject; A gas sensor configured to detect flatulence, and a wearable device; A processor operably coupled to the electromyogram sensor and the gas sensor, the processor being configured to determine the occurrence of a defecation event of the subject based on the abdominal muscle movement signal detected by the electromyogram sensor and the flatulence detected by the gas sensor. A system comprising a processor.

16. The system according to claim 15, wherein the processor is configured to determine the occurrence of a defecation event of the subject based on a series of events including that one of the abdominal muscle movement signal detected by the myogram sensor and the flatulence detected by the gas sensor precedes the other of the abdominal muscle movement signal detected by the myogram sensor and the flatulence detected by the gas sensor.

17. The system according to claim 15 or 16, wherein the wearable device further comprises a base carrying the myogram sensor, the gas sensor, and the processor.

18. A system for detecting a defecation event of a subject, the system comprising: A wearable device configured to be carried on the body of the subject and under lower body clothing worn by the subject, the wearable device comprising: An optical sensor configured to detect increased light when the subject removes the lower body clothing, and an additional sensor configured to detect a stimulus different from light when the increased light is detected by the optical sensor; A processor operably coupled to the optical sensor and the additional sensor, the processor being configured to determine the occurrence of a defecation event based at least in part on the increased light detected by the optical sensor and a signal received from the additional sensor.

19. The system according to claim 18, wherein the wearable device is operable in an active mode and a sleep mode, and the processor is configured to switch the wearable device from the sleep mode to the active mode when it determines that the subject has removed the lower body clothing in response to the increased light detected by the optical sensor.

20. The optical sensor is a first sensor configured to detect light as a first stimulus, the additional sensor is a second sensor configured to detect a second stimulus when the wearable device is in the active mode, the second stimulus is different from the first stimulus, the processor is operably coupled to the second sensor, and the processor is configured to determine the occurrence of a defecation event of the subject based on a signal received from the second sensor. The system according to claim 19.

21. The optical sensor is a first sensor configured to detect light as a first stimulus, the additional sensor is a second sensor configured to detect a second stimulus when the wearable device is in the active mode, the second stimulus is different from the first stimulus, the processor is operably coupled to the second sensor, and the processor is configured to determine the occurrence of a defecation event of the subject based on signals received from the first sensor and the second sensor. The system according to claim 19.

22. The second sensor is an electromyogram sensor. The system according to claim 20 or 21.

23. The wearable device further comprises a patch configured to be carried on the torso of the subject, and the patch carries the optical sensor and the processor. The system according to any one of claims 18 to 21.

24. The wearable device further comprises a belt configured to extend around the torso of the subject, and the belt carries the optical sensor and the processor. The system according to any one of claims 18 to 21.

25. A method for detecting a defecation event of a subject, the method comprising: detecting increased light by an optical sensor of a wearable device carried on the torso of the subject when the subject removes lower body clothing; detecting an abdominal muscle movement signal of the subject by an electromyogram sensor of the wearable device; determining the occurrence of the defecation event based at least in part on the increased light detected when the subject removes the lower body clothing and the detected abdominal muscle movement signal of the subject. A method.

26. The method according to claim 25, further comprising detecting flatulence by the gas sensor of the wearable device, wherein the determination that the defecation event has occurred is at least partially based on the detected flatulence.

27. The method according to claim 25, further comprising detecting the sitting movement of the subject by the inertial measurement unit of the wearable device before detecting the abdominal muscle movement signal of the subject, wherein the determination that the defecation event has occurred is at least partially based on the detected sitting movement.

28. The method according to claim 26, further comprising detecting the standing movement of the subject after detecting the flatulence by the inertial measurement unit of the wearable device, wherein the determination that the defecation event has occurred is at least partially based on the detected standing movement.

29. The method according to claim 25, further comprising detecting a plurality of defecation events of the subject over a certain period.

30. Detecting each of the plurality of defecation events of the subject comprises: detecting the abdominal muscle movement signal of the subject by the myogram sensor of the wearable device; detecting the increased light when the subject takes off the lower body clothing by the optical sensor; determining the occurrence of each of the plurality of defecation events based at least in part on the detected abdominal muscle movement signal of the subject and the detected increased light when the subject takes off the lower body clothing. The method according to claim 29.

31. The method according to any one of claims 25 to 30, wherein detecting the increased light by the optical sensor when the subject takes off the lower body clothing precedes detecting the abdominal muscle movement signal of the subject by the myogram sensor.

32. The method according to any one of claims 25 to 30, wherein detecting the abdominal muscle movement signal of the subject by the myogram sensor precedes detecting the increased light by the optical sensor when the subject takes off the lower body clothing.

33. The method according to any one of claims 25 to 30, further comprising reconfiguring the wearable device from a sleep mode to an active mode based on the increased light detected when the subject removes the lower body garment, wherein in the sleep mode, the electromyogram sensor is inactive, and in the active mode, the electromyogram sensor is configured to detect the abdominal muscle movement signal of the subject.

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