Queen Mary University of London IoT2US (IoT towards Ubiquitous Computing and Science by all) Lab
Objectives: use millimetre wave radar to build a HAR system that can recognize human activities and vital signs. Method: Signal processing algorithms (e.g. FFT, Wavelet transform) are used to get feature map and machine learning and deep learning algorithms are …
Objectives: Use UWB to accurately tracking people, and use the seq2seq (RNN) model to classify daily location-driven activities. Method: Four UWB tags are used to built a system and a Kalman filter is used to improve the positioning accuracy Then …
Objectives: Use 2D lidar to track people and recognize location-driven daily activities. Method: A low cost, 2D, rotating Lidar system is used to collect the lidar radial distance and angle data. After converting the raw data to Cartesian coordinates, Hausdorff …
This is an undergraduate student’s project (by Simon-Peter Biggs) supervised by Dr. Stefan. As the wearable technology market continues to grow the demand for new products health and fitness increases. People are becoming more interested in fitness gadgets that allow …