Wearable Sensing

Wild-SHARD presents a novel Human Activity Recognition (HAR) dataset collected in an uncontrolled, real-world (wild) environment to address the limitations of existing datasets, which often need more non-simulated data. Our dataset comprises a time series of Activities of Daily Living (ADLs) captured using multiple smartphone models such as Samsung Galaxy F62, Samsung Galaxy A30s, Poco X2, One Plus 9 Pro and many more. These devices enhance data variability and robustness with their varied sensor manufacturers.

The dataset involves two sets of participants: a group of twenty skilled drivers aged between 40 and 68, each having a minimum of ten years of driving experience (class 1), and another group consisting of ten novice drivers aged between 18 and 46, who were currently undergoing driving lessons at a driving school (class 2).

The data was recorded using JINS MEME ES_R smart glasses by JINS, Inc. (Tokyo, Japan).

Each file consists of a signals from one sigle ride.

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.