A step counting judgment method and device and a computer-readable storage medium are disclosed. The step counting judgment method comprises: acquiring an acceleration dataset corresponding to each axis of an acceleration sensor in a current step counting cycle; determining a characteristic value corresponding to each axis according to the acceleration dataset corresponding to each axis; comparing characteristic values of different axes, and determining that an axis corresponding to a maximum value in the characteristic values is a characteristic axis; judging validity of step counting data according to the characteristic axis and a reference axis; and performing a step counting operation when the step counting data is valid data. Aims to solve the problem of low step counting accuracy of step counting judgment devices, the present disclosure provides the step counting judgment method and device and the computer-readable storage medium.

Automated detection of head and/or head-affecting body impact events in data collected is performed using instrumented mouthguard devices. For example, in some embodiments the present disclosure relates to training and operation of an impact classifier system, which is configured to identify head affecting impacts from time-series data collected by an instrumented mouthguard device. Some embodiments relate to a two-stage method for processing impacts, including a first stage in which a set of data is classified by such an impact classifier system, and a second stage whereby impacts classified as head affecting impacts are designated a numerical value based on a predefined scale.

A portable apparatus includes an exercise-measurement circuitry that measures exercise-related measurement data related to a user carrying out an exercise, a communication circuitry configured to provide the portable apparatus with wireless communication capability, and a processing circuitry configured to a perform operations. The operations include receiving the exercise-related measurement data from the exercise-measurement circuitry, receiving configuration data from an external user interface apparatus over a bidirectional wireless communication connection established through the communication circuitry and capable of transferring payload data to both directions, processing the exercise-related measurement data according to the received exercise-related parameters in order to obtain advanced exercise-related data, and communicating the advanced exercise-related measurement data to the user interface apparatus over the bidirectional wireless communication connection.

In a configuration in which a holder holding a controller is mounted on a seat part with a plate-shaped member, the exposure of the mounting part of the plate-shaped member on which the holder is mounted is eliminated. A seat device includes a pressure sensor measuring a value relating to the seated person's state, a vibration imparting device performing a vibration imparting operation, an ECU controlling the vibration imparting device corresponding to the measurement result of the pressure sensor, a holder holding the ECU, and a mounting bracket fixed to a lower frame such that the holder is mounted on the lower frame of a seat part. The mounting bracket includes a mounting projection on which side wall of the holder is mounted in a predetermined mounting direction. When the side wall is mounted on the mounting projection, the mounting projection is covered with the side wall.

A system comprising a plurality of neuromuscular sensors, each of which is configured to record a time-series of neuromuscular signals from a surface of a user's body; and at least one computer hardware processor programmed to perform: applying a source separation technique to the time series of neuromuscular signals recorded by the plurality of neuromuscular sensors to obtain a plurality of neuromuscular source signals and corresponding mixing information; providing features, obtained from the plurality of neuromuscular source signals and/or the corresponding mixing information, as input to a trained statistical classifier and obtaining corresponding output; and identifying, based on the output of the trained statistical classifier, and for each of one or more of the plurality of neuromuscular source signals, an associated set of one or more biological structures.

Method, device and system for providing consistent and reliable glucose response information to physiological changes and/or activities is provided to improve glycemic control and health management.

A solution for estimating effectiveness of periodic motion during a physical exercise. such as a running exercise, is disclosed. According to an aspect, a computer-implemented method for estimating the effectiveness of the periodic motion includes: measuring, by using at least one motion sensor, periodic motion of a user, and thus acquiring motion measurement data during a time interval of a physical exercise; transforming the motion measurement data into frequency-domain samples; extracting, amongst the frequency-domain samples by using peak detection, a first subset of frequency-domain samples representing periodic motion; computing a metric indicating a ratio between energy on the first subset of frequency domain samples and energy on other frequency domain samples; and mapping the computed ratio to an effectiveness parameter by using a determined mapping rule and outputting the effectiveness parameter via an interface.

An optical data sensing device of a biological information measuring device, comprising: an optical sensor; a first light emitting device, configured to emit first light away from the optical sensor; and a first opaque isolation component, located between the optical sensor and the first light emitting device, configured to reduce the first light received by the optical sensor. The present invention also discloses an optical data sensing device comprising a plurality of light emitting devices with different emitting directions or wavelengths, to improve the accuracy of biological information measuring.

Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an Analytics Engine that receives one more signal files that include neural signal data of a user based on voltages detected by one or more electrodes on a set of headphones worn by a user. The Analytics Engine preprocesses the data, extracts features from the received data, and feeds the extracted features into one or more machine learning models to generate determined output that corresponds to at least one of a current mental state of the user and a type of facial gesture performed by the user. The Analytics Engine sends the determined output to a computing device to perform an action based on the determined output.

A patient support apparatus comprises a plurality of load cells, a frame supported on the load cells, a mattress, a plurality of air pressure sensors, and a control system. The mattress includes a plurality of inflatable zones positioned on the frame, the mattress and frame cooperating to direct any patient load through the mattress and frame to the load cells. Each of the plurality of air pressure sensors measures the pressure in a respective inflatable zone of the mattress. The control system includes a controller operable to receive a separate signal from each of the plurality of load cells and each of the plurality of air pressure sensors and process the signals to identify motion of the patient. The motion of the patient is further processed by the controller to characterize the nature of the patient motion as a high shear motion or a low shear motion, and based on the characterization of the patient motion, the controller automatically updates a patient profile in a patient record or communicates the information with a caregiver.