A method for allowing or disallowing control of a motion tracking system by means of voice, comprising: digitally processing sound detected by each microphone of the at least one microphone; digitally computing SNR by computing both first energy of a voice signal in the detected sound and second energy of noise in the detected sound; digitally processing electromagnetic waves captured by each antenna of the at least one antenna so as to detect data packets transmitted to the computing apparatus by each sensor of the plurality of sensors, each data packet including RSSI of a respective sensor; digitally computing distance between each sensor and the computing apparatus based on the RSSI of the data packets received from the respective sensor; digitally computing a percentage of sensors of the plurality of sensors having at least one of: a distance exceeding a predetermined maximum distance threshold, and a change in distance exceeding a predetermined maximum changing distance threshold; and digitally setting allowance or disallowance of voice control based on both the SNR computed and the percentage of sensors computed.

The present disclosure discloses a system (100) to accurately determine the calorie consumed during daily activities/exer cise of a user associated with a user device (102) having a plurality of sensors. The system (100) comprises a rules repository (104), an input module (106), a user repository (108), a monitoring module (110), a first analysis module (112), a second analysis module (114), a third analysis module (116) and a calorie consumption module (118). The rules repository (104) stores three sets of pre-determined calculating rules and analysis rules. The input module (106) enables user to enter a plurality of primary user details and store it in the user repository (108). The monitoring module (110) receive a plurality of dynamic user data, a surroundings data and a user activity data using the sensors. The received data along with the primary user details is analysed. A first result, a second result and a third result are calculated post analysis and are added to get a final calorie result.

A first value for each of a plurality of parameters is received, each of the first values being associated with a user and a first day. A second value for each of the plurality of parameters is received, each of the second values being associated with the user and a second day that is subsequent to the first day. For each of the plurality of parameters, a trend indication is determined, the trend indication being based on the first values, the second values, and a first time period. A base weight value for each of the plurality of parameters is determined, the base weight value being based on the first time period and the determined trend indication associated with the one of the plurality of parameters. A mood score is determined, based on the base weight value for each of the plurality of parameters.

A smart ring includes a battery, memory, processing circuitry, a plurality of sensors, a plurality of antennas, and a battery, each coupled to one another and all enclosed in a casing, wherein the processing circuitry is configured to conserve the battery by any of sending data to the cloud service when an application is open on the user device, sending data to the cloud service when a threshold is crossed, waking up processing or communicating when there is a change in motion detected by the accelerometer.

Systems and methods for providing algorithmic equipment and/or accessory recommendations are disclosed herein. In one embodiment, a method providing an equipment or accessory recommendation to an athlete includes: monitoring a first amplitude of a first muscle of the athlete by a first wearable muscle response sensor carried by the athlete; monitoring a second amplitude of a second muscle of the athlete by a second wearable muscle response sensor carried by the athlete; determining a difference between the first amplitude and the second amplitude; comparing the difference to a predetermined amplitude threshold; and based on the comparing, providing an equipment or accessory recommendation to the athlete.

Systems and methods for electronically creating and modifying a fitness plan are disclosed. The method may include receiving electronic user data, collecting electronic fitness data, and displaying a suggestion for a fitness activity based on the electronic user data and the electronic fitness data.

An apparatus device may include a bio-impedance sensor configured to take a bio-impedance measurement from a body of an individual, an optical sensor configured to take an optical measurement from the body of the individual, and a processing device configured to receive a first bio-impedance measurement from the bio-impedance sensor taken during a first period of time and a first optical measurement from the optical sensor taken during the first period of time, receive first location information of the individual during the first period of time, determine a first correlation between a physiological parameter and at least one of the first location, the first bio-impedance measurement, or the first optical measurement, and determine a first level of the physiological parameter based on the first correlation.

Systems and methods for enhancing infection detection and monitoring through decomposed physiological data are disclosed. An example method includes receiving, from a wearable device of a user, physiological data of the user and decomposing the physiological data, by applying a heart rate algorithm, to generate one or more physiological parameters. The example method further includes analyzing, by applying the heart rate algorithm, the one or more physiological parameters to output a period classification, and determining whether or not the period classification is indicative of an infection. The example method further includes, responsive to determining that the period classification is indicative of the infection, displaying, in a user interface, a warning to the user that indicates the infection, and receiving, from the wearable device of the user, additional physiological data of the user to monitor the infection.

Computer implemented biometric methods and systems incorporate sensing biophysical phenomena, translating the phenomena into digital data and transmitting the data to a series of servers operating in an open feedback loop to generate a module. A biometric networking system can include a biometric monitoring cloud computing platform with AI/machine learning augmented models are generated to make user assessments, programs and confidence scores to the healthcare provider systems. The AI/machine learning models can be used by the biometric monitoring network to generate health-related AI processes that analyze relationships treatment techniques and outcomes. AI techniques can be used to calculate movement modeling and confidence scoring including support vector machines, neural networks, and decision trees. The biophysical phenomena may include biometric parameters based on data, such as medical history, exertion, sleep, temperature, cardiovascular events, respiratory events, and muscle and blood pH.

Systems and methods that provide audio feedback in response to gesture validity can provide a more intuitive interface that can train users to correctly complete gestures. Moreover, systems and methods that provide line-specific audio feedback can provide more specific feedback that can allow a user to better understand what sensing line is being contacted. The systems and methods can further include basing the audio feedback based at least in part on obtained activity data, such that invalid and valid feedbacks can provide different sounds dependent on the determined activity state.