PrivacyPreserving Fall Detection with Deep Learning on mmWave Radar Signal Fall is one of the main reasons for body injuries among seniors. Traditional fall detection methods are mainly achieved by wearable and nonwearable techniques, which may cause skin discomfort or invasion of privacy to users. In this paper, we propose an automatic fall detection method with the assist of the mmWave radar signal to solve the aforementioned issues. The radar devices are capable to record the reflection from objects in both the spatial and temporal domain, which can be used to depict the activities of users with the support of a recurrent neural network (RNN) with longshortterm memory (LSTM) units. First, we employ the radar lowdimension embedding (RLDE) algorithm to preprocess the Rangeangle reflection heatmap sequence converted from the raw radar signal for reducing the redundancy in the spatial domain. Then, the processed sequence is split into frames for inputting LSTM units one by one. Eventually, the output from the last LSTM unit is fed in a Softmax layer for classifying different activities. To validate the effectiveness of our proposed method, we construct a radar dataset with the assist of market radar module devices, to implement several experiments. The experimental results demonstrate that, compared to LSTM only and the widely used 3D convolutional neural network (3D CNN), combining RLDE and LSTM can achieve the best detection results with much less computational time consumption. In addition, we extend the proposed method to classify multiple human activities simultaneously and the satisfied performances are observed.

Elderly Health Monitoring System with Fall Detection Using MultiFeature Based Person Tracking The need for personalized surveillance systems for elderly health care has risen drastically. However, recent methods involving the usage of wearable devices for activity monitoring offer limited solutions. To address this issue, we have proposed a system that incorporates a visionbased deep learning solution for elderly surveillance. This system primarily consists of a novel multifeaturebased person tracker (MFPT), supported by an efficient visionbased person fall detector (VPFD). The MFPT encompasses a combination of appearance and motion similarity in order to perform effective target association for object tracking. The similarity computations are carried out through Siamese convolutional neural networks (CNNs) and longshort term memory (LSTM). The VPFD employs histogramoforientedgradients (HoGs) for feature extraction, followed by the LSTM network for fall classification. The cloudbased storage and retrieval of objects is employed allowing the two models to work in a distributed manner. The proposed system meets the objectives of ITU Focus Group on AI for Health (FGAI4H)under the category, xe2x80x9cfalls among the elderlyxe2x80x9d. The system also complies with ITUT F.743.1 standard, and it has been evaluated over benchmarked object tracking and fall detection datasets. The evaluation results show that our system achieves the tracking precision of 94.67% and the accuracy of 98.01% in fall detection, making it practical for health care system use. The HoG featurebased LSTM model is a promising item to be standardized in ITU for fall detection in elderly healthcare management under the requirements and service description provided by ITUT F.743.1.