MIMS-unit is abbreviated for Monitor Independent Movement Summary unit. This measurement is developed to harmonize the processing of accelerometer data from different devices.
In this project, we evaluated the effect of single-site versus multisite motion sensing at seven body locations (both ankles, wrists, hips, and dominant thigh) on the detection of physical behavior recognition using a machine learning algorithm.
Signaligner-Pro is an interactive tool for algorithm-assisted exploration and annotation of raw accelerometer data. The tool can be used by researchers using raw accelerometer data to support research in activity recognition/machine learning, exercise science, and sleep quality research among others.
This paper describes an algorithm that automatically detects stereotypical motor movements (SMM) in individuals on the autism spectrum using three-axis accelerometer data obtained through wearable wireless sensors.
This project aims to detect puffing and smoking behavior in a real-world real-time or near-real-time setting with single or multiple on-body accelerometers.
The game incorporates simple motion primitives such as moving/shaking, tilting and touch gestures into different game tasks: triggering parachute, avoiding obstacles, and stacking a tower. The Game paces are designed to fit the focus attention of kids.