Contactless Human Activity Analysis: An Overview of Different Modalities

Abir, Farhan Fuad; Faisal, Md. Ahasan Atick; Shahid, Omar; Ahmed, Mosabber Uddin

doi:10.1007/978-3-030-68590-4_3

Cited by 7 publications

(4 citation statements)

References 109 publications

(133 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the invasive nature of traditional contact sensing methods, researchers have studied and utilized computer vision technology to achieve contactless human motion perception. In the early days of computer vision-based human motion perception research, image-based detection methods were mainly used [4]; that is, a video stream of human activity is first recorded using a camera, then the video is segmented into frames, and the relevant image processing algorithm is finally used to detect the target human movement. In recent years, video-based human motion perception technology has become increasingly mature and widely used [5,6].…”

Section: Introductionmentioning

confidence: 99%

Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals

2023

View full text Add to dashboard Cite

Detecting human head movement during sleep is important as it can help doctors to assess many physical or mental health problems, such as infantile eczema, calcium deficiency, insomnia, anxiety disorder, and even Parkinson’s disease, and provide useful clues for accurate diagnosis. To obtain the information of head movement during sleep, current solutions either use a camera or require the user to wear intrusive sensors to collect the image or motion data. However, the vision-based schemes rely on light conditions and raise privacy concerns. Many people, including the elderly and infants, may be reluctant to wear wearable devices during sleep. In this paper, we propose Wi-Senser, a nonintrusive and contactless smart monitoring system for detecting head movement during sleep. Wi-Senser directly reuses the existing WiFi infrastructure and exploits the fine-grained channel state information (CSI) of WiFi signals to capture the minute human head movement during sleep without attaching any sensors to the human body. Specifically, we constructed a filtering channel including a Hampel filter, wavelet filter, and mean filter to remove outliers and noises. We propose a new metric of carrier sensitivity to select an optimal subcarrier for recording the change in targeted body movement from 30 candidate subcarriers. Finally, we designed a peak-finding algorithm to capture the real peak set recording the change in human head movement. We designed and implemented Wi-Senser with just one commercial off-the-shelf (COTS) router and one laptop equipped with an Intel 5300 network interface card (NIC). We evaluated the performance of Wi-Senser with 10 volunteers (6 adults and 4 children). Extensive experiments demonstrate that Wi-Senser can achieve 97.95% accuracy for monitoring head movement during sleep. Wi-Senser provides a new solution for achieving noninvasive, continuous, and accurate detection of minute human movement without any additional cost.

show abstract

Section: Introductionmentioning

confidence: 99%

Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals

2023

View full text Add to dashboard Cite

show abstract

“…Hence, the task of hand gesture recognition has been one of the most widely explored areas in the research domain of Human–Computer Interaction (HCI). The detection systems can be classified into two categories—contactless detection systems where the detecting device is kept at a distance from the hand and are not in any sort of contact, and wearable detection systems that are often implemented with several sensors at close contact with the hand 3 .…”

Section: Introductionmentioning

confidence: 99%

“…However, this chip is not usable for detecting gestures at a meter-long distance. In this regard, WiFi has been used as a ubiquitous modality for detecting hand gestures from a greater distance than the Soli chip [6][7][8] ; however, it fails in the precision of detection 3 . On the other hand, several studies have discovered the potential of ultrasound for detecting hand gestures with a clear line of sight [9][10][11] .…”

mentioning

confidence: 99%

“…On the other hand, several studies have discovered the potential of ultrasound for detecting hand gestures with a clear line of sight [9][10][11] . Although in recent years, these RF-based and soundbased modalities have been improved in performance and reliability in particular applications, they are still not dependable in a regular use case where the environmental parameters vary frequently 3 .…”

mentioning

confidence: 99%

See 1 more Smart Citation

Exploiting domain transformation and deep learning for hand gesture recognition using a low-cost dataglove

Faisal

Abir

Ahmed

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

Hand gesture recognition is one of the most widely explored areas under the human–computer interaction domain. Although various modalities of hand gesture recognition have been explored in the last three decades, in recent years, due to the availability of hardware and deep learning algorithms, hand gesture recognition research has attained renewed momentum. In this paper, we evaluate the effectiveness of a low-cost dataglove for classifying hand gestures in the light of deep learning. We have developed a cost-effective dataglove using five flex sensors, an inertial measurement unit, and a powerful microcontroller for onboard processing and wireless connectivity. We have collected data from 25 subjects for 24 static and 16 dynamic American sign language gestures for validating our system. Moreover, we proposed a novel Spatial Projection Image-based technique for dynamic hand gesture recognition. We also explored a parallel-path neural network architecture for handling multimodal data more effectively. Our method produced an F1-score of 82.19% for static gestures and 97.35% for dynamic gestures from a leave-one-out-cross-validation approach. Overall, this study demonstrates the promising performance of a generalized hand gesture recognition technique in hand gesture recognition. The dataset used in this work has been made publicly available.

show abstract

Lunch-Box Preparation Activity Understanding from Motion Capture Data Using Handcrafted Features

Pritom

Rahman

et al. 2022

Sensor- And Video-Based Activity and Behavior Computing

View full text Add to dashboard Cite

Contactless Human Activity Analysis: An Overview of Different Modalities

Cited by 7 publications

References 109 publications

Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals

Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals

Exploiting domain transformation and deep learning for hand gesture recognition using a low-cost dataglove

Lunch-Box Preparation Activity Understanding from Motion Capture Data Using Handcrafted Features

Contact Info

Product

Resources

About