Jing Wei

Jing Wei

PhD Student

Posts by Collection

portfolio

publications

Single-Frequency Ultrasound-Based Respiration Rate Estimation with Smartphones

Published in Computational and mathematical methods in medicine, 2018

Abstract

Respiration monitoring is helpful in disease prevention and diagnosis. Traditional respiration monitoring requires users to wear devices on their bodies, which is inconvenient for them. In this paper, we aim to design a noncontact respiration rate detection system utilizing off-the-shelf smartphones. We utilize the single-frequency ultrasound as the media to detect the respiration activity. By analyzing the ultrasound signals received by the built-in microphone sensor in a smartphone, our system can derive the respiration rate of the user. The advantage of our method is that the transmitted signal is easy to generate and the signal analysis is simple, which has lower power consumption and thus is suitable for long-term monitoring in daily life. The experimental result shows that our system can achieve accurate respiration rate estimation under various scenarios.

Linfei Ge, Jin Zhang, Jing Wei . Computational and mathematical methods in medicine.

You Are How You Sleep: Personalized Sleep Monitoring Based on Wrist Temperature and Accelerometer Data

Published in 13th EAI International Conference on Pervasive Computing Technologies for Healthcare - Demos and Posters, 2019

Abstract

Good sleep is a key component of good health, and as such, how to obtain quality sleep is of concern to many people. Circadian rhythms vary between individuals and play an important role in regulating sleep, however, they are currently not monitored by commercially available wearables. Previous work has shown that circadian rhythm is reflected in changes of wrist temperature. In this work, we present a prototype wristband that measures motion and temperature at the wrist. We developed an algorithm to detect wrist temperature increase onset, which is an indicator of the body preparing for sleep. Our results demonstrate that our algorithm is able to detect wrist temperature increase onset, which appears to occur at the same time for the same person. We also show that temperature increase onset varies between people as does overall temperature patterns between people. The detection of wrist temperature patterns gives us a deeper understanding of the mechanisms underlying sleep and could be a valuable component of a personalized sleep monitoring algorithm.

Jing Wei , Jennifer Boger. 13th EAI International Conference on Pervasive Computing Technologies for Healthcare - Demos and Posters.

Comparison of Gait Speed Estimation of Multiple Sensor-Based Technologies

Published in Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care, 2019

Abstract

In light of our aging population, there is an immediate need for non-obtrusive, continuous, and ubiquitous health monitoring technologies that will enable our population to age with a higher quality of life and independence. Research has demonstrated that gait indicators, such as walking speed, can reflect cognitive and physical functioning. However, gradual changes in such indicators usually go undetected until critical problems arise; being able to detect changes in indicators, such as gait deterioration, of older adults while in their home environments would enable clinicians to tailor more effective and personalized interventions by better understanding user behaviour in real-world settings. Real-world data is essential to enabling our healthcare system to act where patients most need help and to optimize the effect of designed eHealth solutions.

Plinio P Morita, Adson S Rocha, George Shaker, Doojin Lee, Jing Wei, Brandon Fong, Anjali Thatte, Amir-Hossein Karimi, Lin Lin Xu, Avery Ma, Alex Wong, Jennifer Boger Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care.

Comparative Analysis of Gait Speed Estimation Using Wideband and Narrowband Radars, Thermal Camera, and Motion Tracking Suit Technologies

Published in Journal of Healthcare Informatics Research, 2020

Abstract

Research has shown that cognitive and physical functioning of older adults can be reflected in indicators such as walking speed. While changes in cognition, mobility, or health cause changes in gait speed, often gradual variations in walking speed go undetected until severe problems arise. Discrete clinical assessments during clinical consultations often fail to detect changes in day-to-day walking speeds and do not reflect walking speeds in everyday environments, where most of the mobility issues happen. In this paper, we compare four walking speed measurement technologies to a GAITRite mat (gold standard): (1) an ultra wideband radar (covering the band from 3.3 GHz to 10 GHz), (2) a narrow band 24-GHz radar (with a bandwidth of 250 MHz), (3) a perception Neuron Motion Tracking suit, and (4) a thermal camera. Data were collected in parallel using all sensors at the same time for 10 healthy adults for normal and slow walking paces. A comparison of the sensors indicates better performance at lower gait speeds, with offsets (when compared to GAITRite) between 0.1 and 20% for the ultra wideband radar, 1.9 and 17% for the narrowband radar, 0.1 and 38% for the thermal camera, and 1.7 and 38% for the suit. This paper supports the potential of unobtrusive radar-based sensors and thermal camera technologies for ambient autonomous gait speed monitoring for contextual, privacy-preserving monitoring of participants in the community.

PP Morita, AS Rocha, G Shaker, D Lee, J Wei, B Fong, A Thatte, A Karimi, L Xu, A Ma, A Wong, J Boger. Journal of Healthcare Informatics Research.

User Trust in Assisted Decision-Making Using Miniaturized Near-Infrared Spectroscopy

Published in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 2021

Abstract

We investigate the use of a miniaturized Near-Infrared Spectroscopy (NIRS) device in an assisted decision-making task. We consider the real-world scenario of determining whether food contains gluten, and we investigate how end-users interact with our NIRS detection device to ultimately make this judgment. In particular, we explore the effects of different nutrition labels and representations of confidence on participants’ perception and trust. Our results show that participants tend to be conservative in their judgment and are willing to trust the device in the absence of understandable label information. We further identify strategies to increase user trust in the system. Our work contributes to the growing body of knowledge on how NIRS can be mass-appropriated for everyday sensing tasks, and how to enhance the trustworthiness of assisted decision-making systems.

Weiwei Jiang, Zhanna Sarsenbayeva, Niels van Berkel, Chaofan Wang, Difeng Yu, Jing Wei, Jorge Goncalves, Vassilis Kostakos Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. CHI 2021 .

Understanding User Perceptions of Proactive Smart Speakers

Published in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), vol. 5, no. 4, article 185. [Ranking: A*], 2021

Abstract

Voice assistants, such as Amazon’s Alexa and Google Home, increasingly find their way into consumer homes. Their functionality, however, is currently limited to being passive answer machines rather than proactively engaging users in conversations. Speakers’ proactivity would open up a range of important application scenarios, including health services, such as checking in on patient states and triggering medication reminders. It remains unclear how passive speakers should implement proactivity. To better understand user perceptions, we ran a 3-week field study with 13 participants where we modified the off-the-shelf Google Home to become proactive. During the study, our speaker proactively triggered conversations that were essentially Experience Sampling probes allowing us to identify when to engage users. Applying machine-learning, we are able to predict user responsiveness with a 71.6% accuracy and find predictive features. We also identify self-reported factors, such as boredom and mood, that are significantly correlated with users’ perceived availability. Our prototype and findings inform the design of proactive speakers that verbally engage users at opportune moments and contribute to the design of proactive application scenarios and voice-based experience sampling studies.

Jing Wei , Tilman Dingler, Vassilis Kostakos. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. IMWUT .

What Wrist Temperature Tells Us When We Sleep Late: A New Perspective of Sleep Health

Published in 2018 IEEE Ubiquitous Intelligence & Computing, 2021

Abstract

Research has shown that sleep is tied to our internal circadian rhythms and a long-term of misalignment between sleep and circadian rhythms can have harmful effects. Being able to detect circadian patterns is a key component to understanding and supporting a person’s sleep. Wrist temperature has been shown to be correlated to circadian rhythm as well as sleep/wake status. In this paper, we explore the use of wrist temperature to evaluate sleep health of 14 participants over 111 days. Our results demonstrate that our sleep detection algorithm based on wrist temperature can be used to reliably estimate individual’s daily sleep parameters and can be used to support sleep-based phase assessments. A wrist temperature trend-based assessment is also presented to identify when sleep time is misaligned with circadian rythm. This work provides the first steps in future sleep systems that take into account our internal bio-rhythms.

Jing Wei , Jin Zhang, Jennifer Boger. 2018 IEEE Ubiquitous Intelligence & Computing. UIC .

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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