Human Activity Recognition with Wearable Biomedical Sensors in Cyber Physical Systems

Verma, Harshit; Paul, Debdeep; Bathula, Shiva Reddy; Sinha, Sushant K.; Kumar, Sudhir

doi:10.1109/indicon45594.2018.8987001

Cited by 4 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other studies have also analyzed the use of a set of pressure sensors placed in a shoe insole [19] or integrated into an armband, to detect upper limb movements [22]. In turn physiological variables, such as muscular activity [14], breathing [35,37], and heart rate [21,32,36], have also demonstrated their effectiveness in measuring the intensity of the activity and improving the detection of human activity in general. Many commercial wearables use heart rate (HR) based on a photoplethysmography (PPG) circuit embedded in a smartwatch [41], which helps keep HR under a maximum limit during PA and is especially useful for people who have suffered heart failure [6].…”

Section: Ref #Activities Devices and Placement Metricsmentioning

confidence: 99%

“…Traditionally, various methods from the field of signal processing have been leveraged to distill collected sensor data. These have included k-NN [14,30,33,35], random forest (RF), decision tree (DT) [20,38], gaussian mixture model (GMM) and hidden Markov models (HMM) [16,24] or even models based exclusively on thresholds [29] ], all of which requires domain-specific expert knowledge to process raw data. Feature engineering is required to fit a model and this is expensive and not scalable.…”

Section: Ref #Activities Devices and Placement Metricsmentioning

confidence: 99%

See 1 more Smart Citation

Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables

et al. 2022

View full text Add to dashboard Cite

Detecting stress when performing physical activities is an interesting field that has received relatively little research interest to date. In this paper, we took a first step towards redressing this, through a comprehensive review and the design of a low-cost body area network (BAN) made of a set of wearables that allow physiological signals and human movements to be captured simultaneously. We used four different wearables: OpenBCI and three other open-hardware custom-made designs that communicate via bluetooth low energy (BLE) to an external computer—following the edge-computingconcept—hosting applications for data synchronization and storage. We obtained a large number of physiological signals (electroencephalography (EEG), electrocardiography (ECG), breathing rate (BR), electrodermal activity (EDA), and skin temperature (ST)) with which we analyzed internal states in general, but with a focus on stress. The findings show the reliability and feasibility of the proposed body area network (BAN) according to battery lifetime (greater than 15 h), packet loss rate (0% for our custom-made designs), and signal quality (signal-noise ratio (SNR) of 9.8 dB for the ECG circuit, and 61.6 dB for the EDA). Moreover, we conducted a preliminary experiment to gauge the main ECG features for stress detection during rest.

show abstract

Section: Ref #Activities Devices and Placement Metricsmentioning

confidence: 99%

Section: Ref #Activities Devices and Placement Metricsmentioning

confidence: 99%

Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables

et al. 2022

View full text Add to dashboard Cite

show abstract

“…However, the accuracy of the model is not high, and the hardware cost is expensive. The authors [2] proposed wearable biomedical sensors to recognize human activities. They used three classifiers (kNN, linear, and Gaussian kernels) for the training and testing phases.…”

Section: Introductionmentioning

confidence: 99%

Proposing Posture Recognition System Combining MobilenetV2 and LSTM for Medical Surveillance

2022

View full text Add to dashboard Cite

This paper proposes a posture recognition system that can be applied for medical surveillance. The proposed method estimates human posture using mobilenetV2 and long short-term memory (LSTM) to extract the important features of an image. The output of the system was a fully estimated skeleton. We used seven human indoor postures, including lying, sitting, crouching, standing, walking, fighting, and falling, and classified them. The output results are the extraction of the human skeleton and the corresponding labels for the poses. We first experiment with classification using machine learning. The system only achieves approximately 88% accuracy because it is not able to classify similar postures, such as standing and walking. This difference can be caused by the extraction of features for static images, and the machine learning classification algorithm has not reached accuracy with training data. Therefore, we proposed the integration of the LSTM model into the proposed system. LSTM learns the features of the skeleton and provides classification results for postures. As a result, our system improved the accuracy by up to 99%. Similar postures, such as standing and walking, have improved accuracy by up to 7%. In addition, we performed the system on the Jetson Nano hardware. The results show that it can run on a low-profile (44% CPU and 2.1 frames per second) that is capable of applications for remote patient monitoring devices.INDEX TERMS Openpose, long short-term memory, posture detection, skeleton model, intelligent healthcare.

show abstract

Secure Data Sharing in Medical Cyber-Physical system—a Review

Sarode¹,

Karkhile²,

Raskar³

et al. 2022

Futuristic Trends in Networks and Computing Technologies

View full text Add to dashboard Cite

Human Activity Recognition with Wearable Biomedical Sensors in Cyber Physical Systems

Cited by 4 publications

References 11 publications

Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables

Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables

Proposing Posture Recognition System Combining MobilenetV2 and LSTM for Medical Surveillance

Secure Data Sharing in Medical Cyber-Physical system—a Review

Contact Info

Product

Resources

About