An earpiece module includes a physiological sensor, an external energy sensor, a transceiver, a communication module, a data storage component, and a power source. The communication module includes a microphone, a speaker, and a signal processor. The signal processor processes audio information received from a remote source via the transceiver and communicates the processed audio information to a subject via the speaker. The signal processor processes information in real time from the physiological sensor and the external energy sensor, and the signal processor provides biofeedback to the subject based on signals produced by the physiological sensor. The data storage component includes a plurality of algorithms. At least one algorithm focuses processing resources on extracting physiological information from the physiological sensor, at least one algorithm is configured to be modified or uploaded wirelessly via the transceiver, and at least one algorithm is a compression/decompression (CODEC) algorithm.

A muscle assessment protocol can include: attaching one or more surface electromyometry (sEMG) sensors to the skin of a subject to be operably coupled with one or more muscles; operably coupling the one or more sEMG sensors to a computing system; performing the predetermined muscle activity of a muscle assessment protocol that includes a stride rate tuning protocol; monitoring/recording sEMG data of the one or more muscles during the predetermined muscle activity; and providing the sEMG data to the subject such that the subject can improve muscle performance for the predetermined muscle activity by using the sEMG data. The muscle activity includes static or dynamic muscle use. The predetermined muscle activity can be provided to the subject by the computing system.

A device or a system for measuring muscle signals. A system may include at least one processor, memory including computer program code, the memory and the computer program code configured to, with the at least one processor, cause the system to perform at least the following: measure a signal from a muscle to obtain a muscle activation signal; carry out at least one transform on the muscle activation signal to obtain a muscle activation spectrum; and determine a muscle state indicator from the muscle activation spectrum by using signal characteristics from a first and a second band of the muscle activation spectrum.

A method includes receiving location data of a monitoring device when carried by a user and receiving motion data of the monitoring device. The motion data is associated with a time of occurrence and the location data. The method includes processing the received motion data to identify a group of the motion data having a substantially common characteristic and processing the location data for the group of the motion data. The group of motion data by way of processing the location data provides an activity identifier. The motion data includes metric data that identifies characteristics of the motion data. The method includes transferring the activity identifier and the characteristics of the motion data to a screen of a device for display. The activity identifier being a graphical user interface that receives an input for rendering more or less of the characteristics of the motion data.

A control device for an electric bicycle includes a motor, a battery, a micro controller, an acceleration detector and a transmitter electrically connected to each other. A physical-condition monitor detects the rider's heart rate signal. A computer calculates an electric power consumption value from the micro controller, an acceleration value from the acceleration detector, and the heart rate signal to create a motor output adjustment signal which is created according to the practical conditions of the rider. The motor output adjustment signal is sent to the micro controller to adjust the output of the battery to the motor and the assistance force to the bicycle.

An exercise feedback system monitors the performance of athletes wearing a garment with sensors while exercising. The sensors generate physiological data such as muscle activation data, heart rate data, or data describing the athlete's movement. The system extracts features from the physiological data and compares the features with reference exercise data to determine metrics of performance and biofeedback. Based on the physiological data, the system may also modify exercise training programs for the athlete. The exercise feedback system can display the biofeedback using visuals or audio, as well as modified exercise training programs, via the athlete's client device in real time while the athlete is exercising. By reviewing the biofeedback, the athlete may correct the athlete's exercise form to properly use the target muscles for the exercise, or change the certain workouts to personalize the training program.

A living body monitoring system includes an output unit that outputs information regarding a load applied on a user's body on the basis of information regarding a change in an altitude of a point where the user is located and information regarding a change in a blood oxygen amount of the user.

A universal sensor pod provides a native sensing function during a first mode of operation and a secondary sensing function during a second mode of operation. The universal sensor pod may transition from the native sensing function to the secondary sensing function upon detecting that a sensor capable of the secondary sensing function is connected to the universal sensor pod via a smart connector. The universal sensor pod may continue one or more native sensing functions along with the secondary sensing function upon detecting that a sensor capable of the secondary sensing function is connected to the universal sensor pod via a smart connector. A code embedded within the smart connector is transmitted to a processor contained within the universal sensor pod and in response, the processor executes a firmware application that is tailored to the secondary sensing function code in response to receiving the code.

Methods, systems and devices are provided for providing user interface navigation of screen display metrics of a device. In one example, a device is configured for capture of activity data for a user. The device includes a housing and a screen disposed on the housing to display a plurality of metrics which include metrics that characterize the activity captured over time. The device further includes a sensor disposed in the housing to capture physical contact upon the housing. A processor is included to process the physical contact to determine if the physical contact qualifies as an input. The processor enables the screen from an off state when the physical contact qualifies as the input. The screen is configured to display one or more of the plurality of metrics in accordance with a scroll order, and a first metric displayed in response to the physical contact that qualifies as the input.

The present invention relates to a device (100) for measuring a cycling cadence, a method (500) of operating a device (100) for measuring a cycling cadence, and a cycling cadence computer program. The device (100) comprises a motion sensor (such as, e.g., an accelerometer) for detecting a movement of the device (100) and for generating a motion signal (x, y, z) corresponding to the movement; a cadence determination unit (300) for determining cycling cadence based on the motion signal (x, y, z). The device (100) can be worn on the cyclist's wrist or arm (110). The motion sensor in the device is able to pick up the tiny movements of the arm or wrist that correspond to the cadence. Optionally, an algorithm is applied that can derive the cadence from a noisy signal.