A system for monitoring the respiratory activity of a subject, which comprises one or more movement sensors, applied to the thorax of a subject, for generating first signals that are indicative of movement of the thorax of the subject; a receiver for receiving the first generated signals during breathing motion of the subject; and one or more computing devices in data communication with the receiver, for analyzing the breathing motion. The computing device is operable to generate a first breathing pattern from the first signals; divide each respiratory cycle experienced by the subject and defined by the first pattern into a plurality of portions, each of the portions delimited by two different time points and calculate, for each of the plurality of portions of a given respiratory cycle of the first pattern, a slope representing a thorax velocity; derive, from the given respiratory cycle of the first pattern, a pulmonary air flow rate of the subject during predetermined portions of the respiratory cycle; compare between corresponding portions of the first pattern and average flow rates during different phases of the breathing cycle, to calibrate a thorax velocities of the subject with pulmonary air flow rates; and determine respiratory characteristics of the subject for subsequent respiratory cycles experienced by the subject, based on a calculated thorax velocity and the calibration.

A system and a method for predicting kinesthetic outcomes from observed position, posture, behavior or activity of an individual 1602, 1702. The system uses kinesthetic activity sensors 102, 104 each collecting one or more of audio, video, or physiological signals and capturing the activity of the individual or an ambient environment of the individual. These signals are delivered into a computer system 106 implementing a learning routine 108 which constructs one or more personalized kinetic state models 1510 of positional states for the individual and transitions between the positional states, and further develops one or more customized multi-dimensional prediction models 1500 for the individual and uses the multidimensional prediction models to predict behaviors, activities and/or positional changes likely to occur in the future, and provides notice of predicted unsafe or undesired outcomes.

There is provided a human falling detection system including an image sensor, a thermal sensor and a microphone. The image sensor captures an image frame that is used to identify a face and a height-width ratio of a human image. The thermal sensor is used as a filter for filtering out a living body and captures a thermal image that is used to identify a height-width ratio of a human thermal image. The microphone records a time stamp of an abrupt sound appearing.

An information collecting apparatus according to an embodiment includes a sensor data acquisition unit configured to acquire sensor data related to work performed by a worker, a work information data generation unit configured to generate work information data related to work performed by the worker for a specific period of time based on the sensor data acquired by the sensor data acquisition unit, and a danger information specification unit configured to specify work information data related to an action that indicates danger as dangerous work information data indicating dangerous work performed by the worker when an action of the worker in a predetermined period of time, which is indicated by the work information data generated, is the action that indicates danger.

The present invention presents a device for calculating, during one step or each successive step of the gait of a subject, the push-off P.sub.0 of the subject, which is the power per kilogram released by the ankle push-off moment, which comprises: at least one inertial measurement unit (1A, 1B) on one foot of the subject, the inertial measurement unit (1A, 1B) having: at least one accelerometer to measure the vertical and antero-posterior accelerations and/or at least one gyroscope to measure the medic-lateral angular speed data {acute over (α)} during the gait, storage and calculation means (2A) connected to the Inertial measurement unit (1A, 1B), configured to calculate: for the foot, and for the step or each successive step of the gait:—the time of the heel-off and the time of the toe-off,—the push-off P.sub.0, by the Euler's equation stating that the sum of moments acting on the foot taken as a rigid body, being equal to the rate of change of the angular momentum of the foot, with the calculation of the push-off P.sub.0 at the time of the toe-off where the sagittal angular momentum is at its maximum in absolute value, displaying means (2B) connected to the storage and calculation means (2A).

The invention provides a body-worn system that continuously measures pulse oximetry and blood pressure, along with motion, posture, and activity level, from an ambulatory patient. The system features an oximetry probe that comfortably clips to the base of the patient's thumb, thereby freeing up their fingers for conventional activities in a hospital, such as reading and eating. The probe secures to the thumb and measures time-dependent signals corresponding to LEDs operating near 660 and 905 nm. Analog versions of these signals pass through a low-profile cable to a wrist-worn transceiver that encloses a processing unit. Also within the wrist-worn transceiver is an accelerometer, a wireless system that sends information through a network to a remote receiver, e.g. a computer located in a central nursing station.

A user, such as an elderly person, may be assisted by an assistance device in his or her caregiving environment that operates in conjunction with one or more server computers. The assistance device may execute a schedule of assistance actions where each assistance action is associated with a time and is executed at that time to assist the user. An assistance action may present an input request to a user, process a voice input of the user, and analyze the voice input to determine that the voice input corresponds to a negative response event, a positive response event, or a non-response event. Based on the categorization of one or more voice inputs as negative response events, positive response events, or non-response events, it may be determined to notify a caregiver of the user, for example where the user has not responded to a number of assistance actions.

A system may obtain a set of features characterizing a segment of inertial measurement unit (IMU) data generated by an IMU of an ear-wearable device. The system may apply a machine learning model (MLM) that takes the features characterizing the segment of the IMU data as input. The system may determine, based on output values produced by the MLM, whether a user of the ear-wearable device has potentially been subject to physical abuse. The system may then perform an action in response to determining that the user of the ear-wearable device has potentially been subject to physical abuse.

Each accessory device in a set of accessory devices may establish a respective communication link between the accessory device and an ear-wearable device. A particular accessory device in the set of accessory devices may receive data via the communication link between the particular accessory device and the ear-wearable device. The data comprise information generated based on sensor signals from sensors that monitor a user of the ear-wearable device. The accessory devices perform a health monitoring activity based on the data.

Systems and methods of determining a mis-step of a user with gait abnormality, including: calibrating signals corresponding to motion by the user in a controlled environment to identify the gait of the user, detecting a movement of the user to determine a change from the determined gait, detecting at least one physiological signal of the user, identifying motion carried out by the user based on data from at least one wearable sensor, and determining at least one mis-step carried out by the user using a machine learning algorithm trained to determine steps based on the identified motion.