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.

Systems and associated methods are provided for monitoring a user while operating a computing device and providing active feedback to said user regarding health and safety best practices associated with operating said computing device. The methods comprise obtaining user biometric data; converting said biometric data into actionable instances of health and safety user device operation use cases; and interacting with the user based on said actionable instances in order to improve or remedy any deviations from recommended health and safety user device operation practices.

A predictive patient health machine learning model is trained based on baseline health data configured as directed graphs. Patient-healthcare system encounter data formed at least in part by electronic medical records (EMRs) is gathered. The patient-healthcare system encounter data is configured as directed graphs to generate graphed health data and the predictive patient health machine learning model is trained on that graphed health data.

Systems and methods for cardiac pacing during a procedure are disclosed and may include an external pulse generator (EPG) for connecting to a lead. A remote-control module (RCM) wirelessly connected to the EPG may include user inputs to control the EPG. A central processing unit (CPU) with a memory unit for storing code and a processor for executing the code may be included where the CPU is connected to the EPG and RCM. The code may control the EPG in response to user input from the RCM. The CPU may be disposed in the EPG or the RCM, or an interface module (IM) configured to communicate between an otherwise conventional EPG and the RCM. The executable code may perform a continuity test (CT) routine, a capture check (CC) routine, rapid pacing (RP) routine, and/or a back-up pacing (BP) routine, in response to user input from the RCM.

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 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.

A mobility augmentation system assists a user's movement by determining a corresponding electrical stimulation for the movement. A wearable stimulation array includes sensors, electrodes, an electrode multiplexer, and a controller that executes the mobility augmentation system. The sensors measure movement data, and the mobility augmentation system applies a movement model to the measured movement data. The model can determine different electrical actuation instructions depending on the movement stimulated. For example, to stimulate a knee flexion, the movement model output enables a first set of the electrodes to operate as cathodes and a second set of electrodes to operate as anodes. To stimulate a knee extension, the first set of electrodes can be enabled to operate as anodes and a third set of electrodes as cathodes. The user can provide feedback of the applied stimulation, which the system can use to retrain the model and optimize the stimulation to the user.

An external defibrillator system includes one or more compression sensors; one or more physiological sensors; and at least one processor. The at least one processor is configured to: receive and process chest compression signals and physiological signals from the sensors, determine values for chest compression depth and/or chest compression rate based on the received chest compression signals, determine a trend of at least one physiological parameter over a period comprising multiple chest compressions based on the received physiological signals, adjust a target chest compression depth and/or target chest compression rate based on the determined trend of the at least one physiological parameter, compare the determined values for chest compression depth and/or chest compression rate to the adjusted target compression depth and/or the adjusted target compression rate, and provide feedback about the quality of chest compressions performed on the patient.

An external defibrillator system includes one or more compression sensors; one or more physiological sensors; and at least one processor. The at least one processor is configured to: receive and process chest compression signals and physiological signals from the sensors, determine values for chest compression depth and/or chest compression rate based on the received chest compression signals, determine a trend of at least one physiological parameter over a period comprising multiple chest compressions based on the received physiological signals, adjust a target chest compression depth and/or target chest compression rate based on the determined trend of the at least one physiological parameter, compare the determined values for chest compression depth and/or chest compression rate to the adjusted target compression depth and/or the adjusted target compression rate, and provide feedback about the quality of chest compressions performed on the patient.

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.