Activity Recognition in Smart Homes using UWB Radars

Bouchard, Kévin; Maître, Julien; Bertuglia, Camille; Gaboury, Sébastien

doi:10.1016/j.procs.2020.03.004

Cited by 36 publications

(22 citation statements)

References 12 publications

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“…Singh et al [28] introduced exercise-based activities by using mmWave radar. Bouchard et al [66] introduced 15 daily life activities based on ten males. No female participated in their study.…”

Section: Resultsmentioning

confidence: 99%

Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning

Noori¹,

Uddin

Tørresen

2021

IEEE Access

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With recent advances in the field of sensing, it has become possible to build better assistive technologies. This enables the strengthening of eldercare with regard to daily routines and the provision of personalised care to users. For instance, it is possible to detect a person's behaviour based on wearable or ambient sensors; however, it is difficult for users to wear devices 24/7, as they would have to be recharged regularly because of their energy consumption. Similarly, although cameras have been widely used as ambient sensors, they carry the risk of breaching users' privacy. This paper presents a novel sensing approach based on deep learning for human activity recognition using a non-wearable ultra-wideband (UWB) radar sensor. UWB sensors protect privacy better than RGB cameras because they do not collect visual data. In this study, UWB sensors were mounted on a mobile robot to monitor and observe subjects from a specific distance (namely, 1.5-2.0 m). Initially, data were collected in a lab environment for five different human activities. Subsequently, the data were used to train a model using the state-of-the-art deep learning approach, namely long short-term memory (LSTM). Conventional training approaches were also tested to validate the superiority of LSTM. As a UWB sensor collects many data points in a single frame, enhanced discriminant analysis was used to reduce the dimensions of the features through application of principal component analysis to the raw dataset, followed by linear discriminant analysis. The enhanced discriminant features were fed into the LSTMs. Finally, the trained model was tested using new inputs. The proposed LSTM-based activity recognition approach performed better than conventional approaches, with an accuracy of 99.6%. We applied 5-fold cross-validation to test our approach. We also validated our approach on publically available dataset. The proposed method can be applied in many prominent fields, including human-robot interaction for various practical applications, such as mobile robots for eldercare.

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Section: Resultsmentioning

confidence: 99%

Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning

Noori¹,

Uddin

Tørresen

2021

IEEE Access

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“…The CNN algorithm was able to provide an accuracy score of 96.35%. The authors of [ 25 ] made use of UWB radars to create a dataset that contained 10 subjects aged between 22 and 39 years old, performing 15 different activities. The data were collected while other people were still active in the building behind walls.…”

Section: Related Workmentioning

confidence: 99%

Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning

Taylor

Dashtipour

Shah

et al. 2021

Sensors

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The health status of an elderly person can be identified by examining the additive effects of aging along with disease linked to it and can lead to ‘unstable incapacity’. This health status is determined by the apparent decline of independence in activities of daily living (ADLs). Detecting ADLs provides possibilities of improving the home life of elderly people as it can be applied to fall detection systems. This paper presents fall detection in elderly people based on radar image classification by examining their daily routine activities, using radar data that were previously collected for 99 volunteers. Machine learning techniques are used classify six human activities, namely walking, sitting, standing, picking up objects, drinking water and fall events. Different machine learning algorithms, such as random forest, K-nearest neighbours, support vector machine, long short-term memory, bi-directional long short-term memory and convolutional neural networks, were used for data classification. To obtain optimum results, we applied data processing techniques, such as principal component analysis and data augmentation, to the available radar images. The aim of this paper is to improve upon the results achieved using a publicly available dataset to further improve upon research of fall detection systems. It was found out that the best results were obtained using the CNN algorithm with principal component analysis and data augmentation together to obtain a result of 95.30% accuracy. The results also demonstrated that principal component analysis was most beneficial when the training data were expanded by augmentation of the available data. The results of our proposed approach, in comparison to the state of the art, have shown the highest accuracy.

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“…For adult,the standar of respiration rate is described on Table 2 [10]. A study conducted by [11] suggested that changes in sleep position are an alternative to sleep apnea therapy. In addition, BMI and changes in sleep position have an impact on the RPM results, this is related to the accumulation of fat in the anatomy of breathing.…”

Section: Respiratory Rate and Body Mass Index (Bmi)mentioning

confidence: 99%

Ultra Wideband Radar for Respiratory Monitoring on Sleep Position

Ali²,

Suratman³

2021

JMECS

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Sleep apnea is a sleep disorder that has a relation with respiratory system during sleep. One of the sleep apnea characteristic is suddenly stop breathing during sleep. People have the different of respiratory rate (RR) which is affected by sleep positions and body mass index (BMI).There are four sleep positions affecting the respiratory rate (RR). Polysomnography (PSG) is conventionally used to analysis the sleep apnea. This technique requires body contact that might be uncomfortable for the patient. In this study, the Xethru X4M200 radar sensor is proposed as non-contact tool to detect the RR by implementing the Doppler effect. Furthermore, the relation between RR with the sleep position and the BMI are discused. For that purpose, 20 participants (10 males and 10 females) with dierent BMIs and sleep positions are examined by monitoring their chest movement. This method is able to detect the indication of bradypnoea or tachypnoea. Futher systematic study and more participants are required to confirm our results and provide better non-contact technique for RR measurement.

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Activity Recognition in Smart Homes using UWB Radars

Cited by 36 publications

References 12 publications

Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning

Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning

Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning

Ultra Wideband Radar for Respiratory Monitoring on Sleep Position

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