Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living

Saeed, Umer; Shah, Syed Yaseen; Shah, Syed Aziz; Ahmad, Jawad; Alotaibi, Abdullah Alhumaidi; Althobaiti, Turke; Ramzan, Naeem; Alomainy, Akram; Abbasi, Qammer H.

doi:10.3390/electronics10182237

Cited by 34 publications

(17 citation statements)

References 50 publications

(50 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, radar-based fall detection has been widely studied [27,28] and various efficient methods using machine learning techniques have been proposed [29,30]. In recent years, accurate fall detections using radars have been achieved with machine learning methods such as CNN [12,[31][32][33][34], LSTM [13,34], random forest (RF) [35], and support vector machine (SVM) [36]. These classification techniques are properly selected based on the features of the problem and the objectives of the radar data analysis.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Classification of Human Behaviors and Falls in Restroom via Dual Doppler Radar Measurements

Saho

Hayashi

Tsuyama

et al. 2022

Sensors

View full text Add to dashboard Cite

This study presents a radar-based remote measurement system for classification of human behaviors and falls in restrooms without privacy invasion. Our system uses a dual Doppler radar mounted onto a restroom ceiling and wall. Machine learning methods, including the convolutional neural network (CNN), long short-term memory, support vector machine, and random forest methods, are applied to the Doppler radar data to verify the model’s efficiency and features. Experimental results from 21 participants demonstrated the accurate classification of eight realistic behaviors, including falling. Using the Doppler spectrograms (time–velocity distribution) as the inputs, CNN showed the best results with an overall classification accuracy of 95.6% and 100% fall classification accuracy. We confirmed that these accuracies were better than those achieved by conventional restroom monitoring techniques using thermal sensors and radars. Furthermore, the comparison results of various machine learning methods and cases using each radar’s data show that the higher-order derivative parameters of acceleration and jerk, and the motion information in the horizontal direction are the efficient features for behavior classification in a restroom. These findings indicate that daily restroom monitoring using the proposed radar system accurately recognizes human behaviors and allows early detection of fall accidents.

show abstract

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Classification of Human Behaviors and Falls in Restroom via Dual Doppler Radar Measurements

Saho

Hayashi

Tsuyama

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…On the other hand, MSMA-SVM models will be applied to predict other problems such as disease diagnosis and financial risk prediction. In addition, it is expected that the MSMA algorithm can be extended to address different application areas such as photovoltaic cell optimization [103], resource requirement prediction [104,105], and the optimization of deep learning network nodes [106,107].…”

Section: Conclusion Limitations and Future Researchmentioning

confidence: 99%

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

2022

View full text Add to dashboard Cite

In this study, the authors aimed to study an effective intelligent method for employment stability prediction in order to provide a reasonable reference for postgraduate employment decision and for policy formulation in related departments. First, this paper introduces an enhanced slime mould algorithm (MSMA) with a multi-population strategy. Moreover, this paper proposes a prediction model based on the modified algorithm and the support vector machine (SVM) algorithm called MSMA-SVM. Among them, the multi-population strategy balances the exploitation and exploration ability of the algorithm and improves the solution accuracy of the algorithm. Additionally, the proposed model enhances the ability to optimize the support vector machine for parameter tuning and for identifying compact feature subsets to obtain more appropriate parameters and feature subsets. Then, the proposed modified slime mould algorithm is compared against various other famous algorithms in experiments on the 30 IEEE CEC2017 benchmark functions. The experimental results indicate that the established modified slime mould algorithm has an observably better performance compared to the algorithms on most functions. Meanwhile, a comparison between the optimal support vector machine model and other several machine learning methods on their ability to predict employment stability was conducted, and the results showed that the suggested the optimal support vector machine model has better classification ability and more stable performance. Therefore, it is possible to infer that the optimal support vector machine model is likely to be an effective tool that can be used to predict employment stability.

show abstract

“…Radar-based sensing, which has a much wider bandwidth, is also used to detect human activities [ 17 , 18 , 19 ]. The frequency-modulated continuous wave (FMCW) radar uses a bandwidth up to 1.79 GHz, compared to WiFi technology which only utilises a bandwidth up to 20 MHz [ 20 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Reflecting Surface-Based Non-LOS Human Activity Recognition for Next-Generation 6G-Enabled Healthcare System

Saeed

Shah

Khan

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

Human activity monitoring is a fascinating area of research to support autonomous living in the aged and disabled community. Cameras, sensors, wearables, and non-contact microwave sensing have all been suggested in the past as methods for identifying distinct human activities. Microwave sensing is an approach that has lately attracted much interest since it has the potential to address privacy problems caused by cameras and discomfort caused by wearables, especially in the healthcare domain. A fundamental drawback of the current microwave sensing methods is non-line-of-sight environments. They need precise and regulated conditions to detect activity with high precision. In this paper, we suggest the intelligent reflecting surface (IRS) to assure high accuracy activity monitoring in complicated environments where traditional microwave sensing is ineffective. This work is based on reconfigurable IRS that can perform beam-forming/beam-steering and intelligent machine learning algorithms that can accurately recognise several human activities. For experimentation, the transmitter and receiver are positioned on two separate floors of a building. A complicated multi-floor scenario is created in order to test the IRS. Multiple activities such as sitting, standing, and walking are performed on the floor of the building by two individuals, a male and a female. It has been proven through an experiment that IRS technology increases detection accuracy by around 30% compared to conventional microwave sensing without IRS technology.

show abstract

Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living

Cited by 34 publications

References 50 publications

Machine Learning-Based Classification of Human Behaviors and Falls in Restroom via Dual Doppler Radar Measurements

Machine Learning-Based Classification of Human Behaviors and Falls in Restroom via Dual Doppler Radar Measurements

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

Intelligent Reflecting Surface-Based Non-LOS Human Activity Recognition for Next-Generation 6G-Enabled Healthcare System

Contact Info

Product

Resources

About