Activity Recognition Based on Micro-Doppler Signature with In-Home Wi-Fi

Device free activity recognition and monitoring has become a promising research area with increasing public interest in pattern of life monitoring and chronic health conditions. This paper proposes a novel framework for inhome Wi-Fi signal-based activity recognition in e-healthcare applications using passive micro-Doppler (m-D) signature classification. The framework includes signal modeling, Doppler extraction and m-D classification. A data collection campaign was designed to verify the framework where six m-D signatures corresponding to typical daily activities are sucessfully detected and classified using our software defined radio (SDR) demo system. Analysis of the data focussed on potential discriminative characteristics, such as maximum Doppler frequency and time duration of activity. Finally, a sparsity induced classifier is applied for adaptting the method in healthcare application scenarios and the results are compared with those from the well-known Support Vector Machine (SVM) method

Transfer Learning from Audio Deep Learning Models for Micro-Doppler Activity Recognition

This paper presents a mechanism to transform radio micro-Doppler signatures into a pseudo-audio representation, which results in significant improvements in transfer learning from a deep learning model trained on audio. We also demonstrate that transfer learning from a deep learning model trained on audio is more effective than transfer learning from a model trained on images, which suggests machine learning methods used to analyse audio can be leveraged for micro-Doppler. Finally, we utilise an occlusion method to gain an insight into how the deep learning model interprets the micro-Doppler signatures and the subsequent pseudo-audio representations

Doppler based detection of multiple targets in passive WiFi radar using undetermined blind source separation

Passive approaches for detecting and localizing people in wireless environments have attracted significant attention because of its diverse application in healthcare, security and robotics in recent years. However, within indoor environments multiple people moving in close proximity to each other often impedes the utility of such approaches. In this paper we present a new method for identifying multiple human targets in Wi-Fi passive radar systems using only a single receive channel to detect Doppler returns. The technique is based on tree-structure sparse underdetermined blind source separation and utilizes proximal alternating methods in a convex optimization field. Firstly, we show proof-of-principle simulation results for two targets moving within a typical indoor scenario and compare the results with those from the well-known independent component analysis (ICA). Secondly, we validate the simulation outputs using real-world experimental data. The results demonstrate the effectiveness of the proposed technique for device-free detection of multiple targets in the indoor wireless landscape

Automatic Wireless Fall Detection System

According to an article published by NewsUSA on the study conducted by the National Institute on Aging (NIA), more than one older adult falls each year and more than 80 percent of the falls happen in the bathroom. So there is a high need for systems that can detect fall and report it in real time especially inside bathrooms. All the systems that are currently available are either wearable devices or involve technologies that are either too expensive to install or intrude privacy. Also, dependency on wearable devices for automatic fall detection greatly limits the quality of life of older adults. In order to overcome these limitations and improve the quality of life of older adults, we present a new fall detection system that is wireless, cheap and efficient in detecting falls. The system we propose in this report is built using Micro-Doppler radar. We utilize the intensity captured by the Doppler sensor to determine the probability of fall. We developed a machine learning model that consumes encoding of captured intensities and determines the activity as fall and non fall. The model determines and reports a possible fall within 1 second. We also tested our encoding model approach on Android smartphone by capturing accelerometer and gyroscope data. The results obtained from both the experiments were very promising and encouraging

Activity Recognition Based on Micro-Doppler Signature with In-Home Wi-Fi

Device free activity recognition and monitoring has become a promising research area with increasing public interest in pattern of life monitoring and chronic health conditions. This paper proposes a novel framework for in-home Wi-Fi signal-based activity recognition in e-healthcare applications using passive micro-Doppler (m-D) signature classification. The framework includes signal modeling, Doppler extraction and m-D classification. A data collection campaign was designed to verify the framework where six m-D signatures corresponding to typical daily activities are sucessfully detected and classified using our software defined radio (SDR) demo system. Analysis of the data focussed on potential discriminative characteristics, such as maximum Doppler frequency and time duration of activity. Finally, a sparsity induced classifier is applied for adaptting the method in healthcare application scenarios and the results are compared with those from the well-known Support Vector Machine (SVM) method