Fast and Scalable Human Pose Estimation using mmWave Point Cloud

An, Sizhe; Ogras, Umit Y.

doi:10.48550/arxiv.2205.00097

Cited by 2 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Advances in sensing and processing technologies facilitate the long-term objective measurement of symptoms. Hence, new sensors, wearable devices, video capture/processing systems, and mobile technologies enable a wide range of monitoring, diagnosis, and rehabilitation applications [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. Indeed, the number of publications that report using wearable and mobile technology for PD research increased by more than 20-fold from 2008 to 2021, as discussed in Section 4 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Survey of Research Trends in Technology Usage for Parkinson’s Disease

Deb

Bhat

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

Parkinson’s disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The complexity of PD pathology is amplified due to its dependency on patient diaries and the neurologist’s subjective assessment of clinical scales. A significant amount of recent research has explored new cost-effective and subjective assessment methods pertaining to PD symptoms to address this challenge. This article analyzes the application areas and use of mobile and wearable technology in PD research using the PRISMA methodology. Based on the published papers, we identify four significant fields of research: diagnosis, prognosis and monitoring, predicting response to treatment, and rehabilitation. Between January 2008 and December 2021, 31,718 articles were published in four databases: PubMed Central, Science Direct, IEEE Xplore, and MDPI. After removing unrelated articles, duplicate entries, non-English publications, and other articles that did not fulfill the selection criteria, we manually investigated 1559 articles in this review. Most of the articles (45%) were published during a recent four-year stretch (2018–2021), and 19% of the articles were published in 2021 alone. This trend reflects the research community’s growing interest in assessing PD with wearable devices, particularly in the last four years of the period under study. We conclude that there is a substantial and steady growth in the use of mobile technology in the PD contexts. We share our automated script and the detailed results with the public, making the review reproducible for future publications.

show abstract

Section: Introductionmentioning

confidence: 99%

“…For example, motion-capturing systems such as Microsoft Kinect [ 64 ] and Vicon 3D [ 65 ] use structured light for analysis. Similarly, radar-based systems use radio-frequency signals to monitor the motion of subjects [ 32 , 37 ]. A system of multiple IMUs, even camera-based 3D setups, can also capture a patient’s movement.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Survey of Research Trends in Technology Usage for Parkinson’s Disease

Deb

Bhat

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similarly, Refs. 15,16 , utilized mmWave data to reconstruct human poses. They have achieved great success in reconstructing the human pose through pose estimation.…”

mentioning

confidence: 99%

Evaluation of deep learning models in contactless human motion detection system for next generation healthcare

Song

Taylor

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Recent decades have witnessed the growing importance of human motion detection systems based on artificial intelligence (AI). The growing interest in human motion detection systems is the advantages of automation in the monitoring of patients remotely and giving warnings to doctors promptly. Currently, wearable devices are frequently used for human motion detection systems. However, such devices have several limitations, such as the elderly not wearing devices due to lack of comfort or forgetfulness and/or battery limitations. To overcome the problems of wearable devices, we propose an AI-driven human motion detection system (deep learning-based system) using channel state information (CSI) extracted from Radio Frequency (RF) signals. The main contribution of this paper is to improve the performance of the deep learning models through techniques, including structure modification and dimension reduction of the original data. In this work, We firstly collected the CSI data with the center frequency 5.32 GHz and implemented the structure of the basic deep learning network in our previous work. After that, we changed the basic deep learning network by increasing the depth, increasing the width, adapting some advanced network structures, and reducing dimensions. After finishing those modifications, we observed the results and analyzed how to further improve the deep learning performance of this contactless AI-enabled human motion detection system. It can be found that reducing the dimension of the original data can work better than modifying the structure of the deep learning model.

show abstract

Fast and Scalable Human Pose Estimation using mmWave Point Cloud

Cited by 2 publications

References 11 publications

A Systematic Survey of Research Trends in Technology Usage for Parkinson’s Disease

A Systematic Survey of Research Trends in Technology Usage for Parkinson’s Disease

Evaluation of deep learning models in contactless human motion detection system for next generation healthcare

Contact Info

Product

Resources

About