Abstract: A wearable garment with sensors attached to obtain physiological data. The sensors are incorporated to form a body area sensor network to obtain the data. This provides patients with improved health monitoring by aggregating multiple interconnected nodes on a human body for sensorimotor measurements and provides patients with quantitative measurements of their progress. The data is obtained in a way that allows for the number of transmissions to be reduced thereby conserving the energy of the wearable devices. This is made possible by each sensor reducing the number of samples by eliminating predictable samples and configuring the sensors to pack the data efficiently. A neural network can determine whether a sample can be skipped or needs to be reported. A long short term memory architecture creates a waveform for a given snapshot of samples based on the previous samples regardless of whether these samples were reported or predicted.

Abstract: This system is a network of sensor nodes with multiple sensors at each node. The nodes are used in combination with a wearable garment to enable multiple types of data to be combined together to produce a fuller picture of a body's physiological state; such as during physical therapy. In addition, the system utilizes acoustic imaging to measure muscle activation. The system transmit this data to a host computer to visualize various data comparisons.

Abstract: This system is a network of sensor nodes with multiple sensors at each node. The nodes are used in combination with a wearable garment to enable multiple types of data to be combined together to produce a fuller picture of a body's physiological state; such as during physical therapy. In addition, the system utilizes acoustic imaging to measure muscle activation. The system transmit this data to a host computer to visualize various data comparisons.

Abstract: An embodiment of the invention provides a method where input is received in sensors on a glove, where the sensors include a force sensor, a flex sensor, and/or a range-of-motion IMU sensor. The input is sent from the sensors on the glove to a processor on the glove. The input is analyzed with the processor to determine an exercise being performed by a user of the glove. A trained neural network is used to analyze the input from the sensors; and, the orientation of the glove is recognized with the trained neural network. The input and the orientation are classified as a grip exercise and/or a rotation exercise.