Unobtrusive Human Fall Detection System Using mmWave Radar and Data Driven Methods

Article Engineering, Biomedical

An Unobtrusive Human Activity Recognition System Using Low Resolution Thermal Sensors, Machine and Deep Learning

Ariyamehr Rezaei et al.

Summary: In this study, an unobtrusive monitoring system using infrared technology was developed to accurately detect human activities, locations, and critical activities such as falling and lying on floor.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2023)

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Article Automation & Control Systems

mmFall: Fall Detection Using 4-D mmWave Radar and a Hybrid Variational RNN AutoEncoder

Feng Jin et al.

Summary: In this article, a novel fall detection system mmFall is proposed, which combines millimeter-wave radar sensor and a hybrid variational recurrent neural network autoencoder. With the use of generative approach and semisupervised method, the randomness in radar data and the difficulties in fall data collection/labeling are overcome. Experimental results show that the proposed model outperforms baselines and has a low false alarm rate.

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2022)

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Article Computer Science, Information Systems

2-D LIDAR-Based Approach for Activity Identification and Fall Detection

Mondher Bouazizi et al.

Summary: In this article, a novel approach using 2-D LIDAR and deep learning is proposed for activity detection in the monitoring of elderly people living alone. The approach processes the collected data using 2-D LIDAR and transforms them into time-series and image representations. LSTM networks are used for person identification and activity recognition, while a CNN network is fine-tuned for fall detection. The experimental results show high accuracy in multiclass activity detection, fall detection, person identification, and unsteady walk detection.

IEEE INTERNET OF THINGS JOURNAL (2022)

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Article Computer Science, Information Systems

Fall Detection System Using Millimeter-Wave Radar Based on Neural Network and Information Fusion

Yicheng Yao et al.

Summary: This paper presents a fall detection system based on frequency-modulated continuous-wave (FMCW) radar, which identifies falls by extracting the time-space-velocity features of radar signals. The results on the test set show a recall of 0.983 and a precision of 0.975 under new environments and new subjects.

IEEE INTERNET OF THINGS JOURNAL (2022)

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Proceedings Paper Acoustics

REAL-TIME FALL DETECTION USING MMWAVE RADAR

Wenxuan Li et al.

Summary: This paper introduces a real-time fall detection system using millimeter wave signal, which improves system performance and robustness through spatial-temporal processing and data augmentation, and eventually designs a lightweight convolutional neural network for real-time fall detection.

2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) (2022)

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Proceedings Paper Automation & Control Systems

Fall Detection System Based on Millimeter Wave Radar and Machine Learning

Jiye Liang et al.

Summary: This paper presents a fall detection system for the elderly using millimeter wave radar technology and machine learning algorithm. The system achieves high recognition accuracy by sampling human point cloud data and training the model with random forest and BP neural network. The system also utilizes 4G communication technology to transmit data to a cloud platform. The experiments show that the system effectively detects human falls and has strong robustness.

2022 6TH INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION SCIENCES (ICRAS 2022) (2022)

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Article Chemistry, Analytical