Wearable Sensing

Highly accurate and lightweight automated movements quality assessment is essential for home rehabilitation patients. We propose a method for the assessment and quantification of movement quality based on the differential feature segments, the objective  is to emulate the expert evaluations of physicians as closely as possible with minimal data features. Employing the Gaussian mixture model (GMM) to divide continuous trend time-series data into fragment features, defined as feature segments.

The performance, repeatability, and stability of the sensor were measured,Sensors are applied to gesture recognition and human-computer interaction. By utilizing wavelength division multiplexing technology, multiple sensors measure finger movements and improve recognition accuracy through backpropagation neural network algorithms. Five commonly used gestures were recognized and the sensor posture was determined based on light intensity.

A specially designed waist-worn device with accelerometer, gyroscope, and pressure sensor was utilized to collect information about 18 ADLs and 16 fall types. The falls protocol has been performed in our lab to replicate realistic situations that typically affect workers and older people. In contrast to other datasets that are accessible to the public, we included a new task in the falls, syncope, since it has a high mortality rate among the elderly and is linked to falls. As such, we must take it into account and include it in our fall detection system.

While resistance training promotes muscle hypertrophy, accessibility of equipment is a barrier. This study evaluated a wearable VAriable Resistance Suit (VARS) as a novel and alternative method to achieve muscle hypertrophy. It was hypothesized that by providing adjustable, bi-directional and speed dependent resistance, VARS can target specific muscles to improve muscle strength, size and mass in an accessible and portable device.