Kitchen Activity Detection for Healthcare using a Low-Power Radar-Enabled Sensor Network

Luo, Fei; Poslad, Stefan; Bodanese, Eliane

doi:10.1109/icc.2019.8761484

Cited by 24 publications

(22 citation statements)

References 17 publications

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“…Standing, walking, sleeping, eating, bathing and postures data are collected for developing a ML/DL model to recognize the fall, sleep disorder and diet of the patient. From the literature review, heart problems, fall, sleep disorder and eating habits can be recognized by non-contact wireless sensing platforms [ 14 , 15 , 17 , 20 , 22 , 23 , 25 , 26 , 27 , 28 , 30 , 31 , 33 , 34 , 36 , 38 , 40 , 45 , 47 , 48 , 49 , 57 , 61 , 62 , 63 ].…”

Section: Methodsmentioning

confidence: 99%

“…This system was developed using an off-the-shelf commercial Wi-Fi router, omnidirectional antennas and a network interface card (NIC) for imminent body-centric communication [ 46 ]. A low cost, non-intrusive and minimal low-power radar-based sensing system that uses a novel approach for human activity recognition in the home was developed that investigates fifteen different challenging activities performed inside the kitchen [ 47 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Wi-Fi sensing using commercial hardware is widely used because it is an inexpensive and easily available solution. Human activities have been monitored and classified in existing research by ML and DL algorithms having accuracies over 90% [ 28 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ]. The average accuracy of promising non-contact technologies for monitoring human activities is shown in Figure 1 .…”

Section: Literature Reviewmentioning

confidence: 99%

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A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19

Khan

Zhang

et al. 2020

Micromachines

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The rapid spread of the novel coronavirus disease, COVID-19, and its resulting situation has garnered much effort to contain the virus through scientific research. The tragedy has not yet fully run its course, but it is already clear that the crisis is thoroughly global, and science is at the forefront in the fight against the virus. This includes medical professionals trying to cure the sick at risk to their own health; public health management tracking the virus and guardedly calling on such measures as social distancing to curb its spread; and researchers now engaged in the development of diagnostics, monitoring methods, treatments and vaccines. Recent advances in non-contact sensing to improve health care is the motivation of this study in order to contribute to the containment of the COVID-19 outbreak. The objective of this study is to articulate an innovative solution for early diagnosis of COVID-19 symptoms such as abnormal breathing rate, coughing and other vital health problems. To obtain an effective and feasible solution from existing platforms, this study identifies the existing methods used for human activity and health monitoring in a non-contact manner. This systematic review presents the data collection technology, data preprocessing, data preparation, features extraction, classification algorithms and performance achieved by the various non-contact sensing platforms. This study proposes a non-contact sensing platform for the early diagnosis of COVID-19 symptoms and monitoring of the human activities and health during the isolation or quarantine period. Finally, we highlight challenges in developing non-contact sensing platforms to effectively control the COVID-19 situation.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19

Khan

Zhang

et al. 2020

Micromachines

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“…Lea et al [32] used TCNs to build an Encoder-Decoder model for human action segmentation and recognition upon videos. Luo et al [1] used a CNN to classify Radar frequency spectrograms for an indoor HAR comprising of fifteen different activities. Some of the above algorithms have also been used in trajectory-based human activity recognition as presented in Section II-C.…”

Section: B Machine Learning Algorithmsmentioning

confidence: 99%

“…H UMAN activity recognition (HAR) has a wide range of applications in surveillance, healthcare, smart home, intelligent control, etc. For example, doctors and dietitians can provide advice remotely for patients and customers based on their daily dietary activities [1]. People can use gesture recognition [2], [3] to contactlessly interact with electronic devices such as TV, computers, smart glasses, etc.…”

Section: Introductionmentioning

confidence: 99%

Temporal Convolutional Networks for Multiperson Activity Recognition Using a 2-D LIDAR

Luo

Poslad

Bodanese

2020

IEEE Internet Things J.

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Motion trajectories contain rich information about human activities. We propose to use a 2D LIDAR to perform multiple people activity recognition simultaneously by classifying their trajectories. We clustered raw LIDAR data and classified the clusters into human and non-human classes in order to recognize humans in a scenario. For the clusters of humans, we implemented the Kalman Filter to track their trajectories which are further segmented and labelled with corresponding activities. We introduced spatial transformation and Gaussian noise for trajectory augmentation in order to overcome the problem of unbalanced classes and boost the performance of human activity recognition (HAR). Finally, we built two neural networks including a long short-term memory (LSTM) network and a temporal convolutional network (TCN) to classify trajectory samples into 15 activity classes collected from a kitchen. The proposed TCN achieved the best result of 99.49% in overall accuracy. In comparison, the TCN is slightly superior to the LSTM network. Both the TCN and the LSTM network outperform hidden Markov Model (HMM), dynamic time warping (DTW), and support vector machine (SVM) with a wide margin. Our approach achieves a higher activity recognition accuracy than the related work.

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Spectrogram-Based Approach with Convolutional Neural Network for Human Activity Classification

Sassi,

Haleem,

Pecchia

2024

IFMBE Proceedings

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Kitchen Activity Detection for Healthcare using a Low-Power Radar-Enabled Sensor Network

Cited by 24 publications

References 17 publications

A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19

A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19

Temporal Convolutional Networks for Multiperson Activity Recognition Using a 2-D LIDAR

Spectrogram-Based Approach with Convolutional Neural Network for Human Activity Classification

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