FMCW Radar-Based Anomaly Detection in Toilet by Supervised Machine Learning Classifier

Wataru, Takabatake; Yamamoto, Kohei; Toyoda, Kentaroh; Ohtsuki, Tomoaki; Shibata, Yohei; Nagate, Atsushi

doi:10.1109/globecom38437.2019.9014123

Cited by 12 publications

(13 citation statements)

References 12 publications

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“…The classification of human behaviors and falls in a restroom with above 95% was demonstrated. This result shows significant improvement over other conventional studies on radar-based restroom monitoring [14][15][16].…”

mentioning

confidence: 66%

“…However, as stated in the Introduction, most studies on motion recognition and fall detection studies using radar have not considered their application to the restroom. Thus, there are only several studies on radar-based restroom monitoring [14][15][16]. For example, in [15], the classification accuracy of seven types of behaviors and falls was approximately 60%.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, there are only several studies on radar-based restroom monitoring [14][15][16]. For example, in [15], the classification accuracy of seven types of behaviors and falls was approximately 60%. The low accuracy of conventional radar systems may be due either to the use of range information or to the use of classical detection/classification methods.…”

Section: Related Workmentioning

confidence: 99%

“…However, research on the use of radar in restrooms is quite limited. Though the detection of falls in restrooms using radar systems or the classification of normal and abnormal behaviors by range (distance) information [14,15] have been studied based on classical signal detection or discriminant analysis methods, it is difficult to determine what is happening in a restroom because only abnormal behaviors, such as falls, are detected. Moreover, their accuracy and practicality were both insufficient.…”

Section: Introductionmentioning

confidence: 99%

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

Saho

Hayashi

Tsuyama

et al. 2022

Sensors

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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.

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mentioning

confidence: 66%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Saho

Hayashi

Tsuyama

et al. 2022

Sensors

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“…Video/image recorders were usually used for detecting washing hands [ 35 , 39 , 42 , 43 , 44 ], washing face [ 36 ] and dressing [ 45 , 46 , 47 , 48 ]. Radar sensors were used for detecting standing/sitting while showering [ 49 ], entering the bathtub [ 49 , 50 ] and sitting on/leaving toilet [ 51 ]. IMU sensors were attached to the water pipe for detecting washing and bathing [ 52 ], on the robot for detecting dressing [ 46 ], and in the soap bar for hand washing [ 37 ].…”

Section: Methodsmentioning

confidence: 99%

Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations

Zhang

D'Haeseleer

Coelho

et al. 2021

Sensors

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This article provides a systematic review of studies on recognising bathroom activities in older adults using wearable sensors. Bathroom activities are an important part of Activities of Daily Living (ADL). The performance on ADL activities is used to predict the ability of older adults to live independently. This paper aims to provide an overview of the studied bathroom activities, the wearable sensors used, different applied methodologies and the tested activity recognition techniques. Six databases were screened up to March 2020, based on four categories of keywords: older adults, activity recognition, bathroom activities and wearable sensors. In total, 4262 unique papers were found, of which only seven met the inclusion criteria. This small number shows that few studies have been conducted in this field. Therefore, in addition, this critical review resulted in several recommendations for future studies. In particular, we recommend to (1) study complex bathroom activities, including multiple movements; (2) recruit participants, especially the target population; (3) conduct both lab and real-life experiments; (4) investigate the optimal number and positions of wearable sensors; (5) choose a suitable annotation method; (6) investigate deep learning models; (7) evaluate the generality of classifiers; and (8) investigate both detection and quality performance of an activity.

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Machine Learning for Healthcare Radars: Recent Progresses in Human Vital Sign Measurement and Activity Recognition

Ahmed,

Cho

2024

IEEE Commun. Surv. Tutorials

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The unprecedented non-contact, non-invasive, and privacy-preserving nature of radar sensors has enabled various healthcare applications, including vital sign monitoring, fall detection, gait analysis, activity recognition, fitness evaluation, and sleep monitoring. Machine learning (ML) is revolutionizing every domain, with radar-based healthcare being no exception. Progress in the field of healthcare radars and ML is complementing the existing radar-based healthcare industry. This article provides an overview of ML usage for two major healthcare applications: vital sign monitoring and activity recognition. Vital sign monitoring is the most promising healthcare application of radar, as it can predict several chronic cardiac and respiratory diseases. Activity recognition is also a prominent application since the inability to perform activities may result in critical suffering. The article presents an overview of commercial radars, radar hardware, and historical progress of healthcare radars, followed by the usage of ML for healthcare radars. Subsequently, the paper discusses how ML can overcome the limitations of conventional radar data processing chains for healthcare radars. The article also touches upon recent generative ML concepts used in healthcare radars. Among several interesting findings, it was discovered that ML does not completely replace existing vital sign monitoring algorithms; rather, ML is deployed to overcome the limitations of traditional algorithms. On the other hand, activity recognition always relies on ML approaches. The most widely used algorithms for both applications are Convolutional Neural Network (CNN) followed by Support Vector Machine (SVM). Generative AI has the capability to augment data and is expected to have a significant impact soon. Recent trends, lessons learned from these trends, and future directions for both healthcare applications are presented in detail. Finally, the future work section discusses a wide range of healthcare topics for humans, ranging from neonates to elderly individuals.

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FMCW Radar-Based Anomaly Detection in Toilet by Supervised Machine Learning Classifier

Cited by 12 publications

References 12 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

Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations

Machine Learning for Healthcare Radars: Recent Progresses in Human Vital Sign Measurement and Activity Recognition

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