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1. Optomechanical inertial sensors

   https://doi.org/10.1364/AO.393061  

   Hines, Adam; Richardson, Logan; Wisniewski, Hayden; Guzman, Felipe (June 2020, Applied Optics)

   We present a performance analysis of compact monolithic optomechanical inertial sensors that describes their key fundamental limits and overall acceleration noise floor. Performance simulations for low-frequency gravity-sensitive inertial sensors show attainable acceleration noise floors on the order of $1\times <\#comment/>{10}^{-<\#comment/>11}\mathrm{m}/{\mathrm{s}}^2\sqrt{\mathrm{H}\mathrm{z}}$. Furthermore, from our performance models, we devised an optimization approach for our sensor designs, sensitivity, and bandwidth trade space. We conducted characterization measurements of these compact mechanical resonators, demonstrating $\text{mQ}$-products at levels of 250 kg, which highlight their exquisite acceleration sensitivity. 

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2. HAWAD: Hand Washing Detection using Wrist Wearable Inertial Sensors

   Mondol, A; Stankovic, J. (June 2020, International Conference on Distributed Computing in Sensor Systems and workshops)

   Hand hygiene is crucial in preventing the spread of infections and diseases. Lack of hand hygiene is one of the major reasons for healthcare associated infections (HAIs) in hospitals. Adherence to hand hygiene compliance by the workers in the food business is very important for preventing food-borne illness. In addition to healthcare settings and food businesses, hand washing is also vital for personal well-being. Despite the importance of hand hygiene, people often do not wash hands when necessary. Automatic detection of hand washing activity can facilitate justin-time alerts when a person forgets to wash hands. Monitoring hand washing practices is also essential in ensuring accountability and providing personalized feedback, particularly in hospitals and food businesses. Inertial sensors available in smart wrist devices can capture hand movements, and so it is feasible to detect hand washing using these devices. However, it is challenging to detect hand washing using wrist wearable sensors since hand movements are associated with a wide range of activities. In this paper, we present HAWAD, a robust solution for hand washing detection using wrist wearable inertial sensors. We leverage the distribution of penultimate layer output of a neural network to detect hand washing from a wide range of activities. Our method reduces false positives by 77% and improves F1-score by 30% compared to the baseline method. 

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3. Characterizing Human Box-Lifting Behavior Using Wearable Inertial Motion Sensors

   https://doi.org/10.3390/s20082323  

   Hlucny, Steven D.; Novak, Domen (April 2020, Sensors)

   null (Ed.)

   Although several studies have used wearable sensors to analyze human lifting, this has generally only been done in a limited manner. In this proof-of-concept study, we investigate multiple aspects of offline lift characterization using wearable inertial measurement sensors: detecting the start and end of the lift and classifying the vertical movement of the object, the posture used, the weight of the object, and the asymmetry involved. In addition, the lift duration, horizontal distance from the lifter to the object, the vertical displacement of the object, and the asymmetric angle are computed as lift parameters. Twenty-four healthy participants performed two repetitions of 30 different main lifts each while wearing a commercial inertial measurement system. The data from these trials were used to develop, train, and evaluate the lift characterization algorithms presented. The lift detection algorithm had a start time error of 0.10 s ± 0.21 s and an end time error of 0.36 s ± 0.27 s across all 1489 lift trials with no missed lifts. For posture, asymmetry, vertical movement, and weight, our classifiers achieved accuracies of 96.8%, 98.3%, 97.3%, and 64.2%, respectively, for automatically detected lifts. The vertical height and displacement estimates were, on average, within 25 cm of the reference values. The horizontal distances measured for some lifts were quite different than expected (up to 14.5 cm), but were very consistent. Estimated asymmetry angles were similarly precise. In the future, these proof-of-concept offline algorithms can be expanded and improved to work in real-time. This would enable their use in applications such as real-time health monitoring and feedback for assistive devices. 

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4. mTeeth: Identifying Brushing Teeth Surfaces Using Wrist-Worn Inertial Sensors

   https://doi.org/10.1145/3463494  

   Akther, Sayma; Saleheen, Nazir; Saha, Mithun; Shetty, Vivek; Kumar, Santosh (June 2021, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   null (Ed.)

   Ensuring that all the teeth surfaces are adequately covered during daily brushing can reduce the risk of several oral diseases. In this paper, we propose the mTeeth model to detect teeth surfaces being brushed with a manual toothbrush in the natural free-living environment using wrist-worn inertial sensors. To unambiguously label sensor data corresponding to different surfaces and capture all transitions that last only milliseconds, we present a lightweight method to detect the micro-event of brushing strokes that cleanly demarcates transitions among brushing surfaces. Using features extracted from brushing strokes, we propose a Bayesian Ensemble method that leverages the natural hierarchy among teeth surfaces and patterns of transition among them. For training and testing, we enrich a publicly-available wrist-worn inertial sensor dataset collected from the natural environment with time-synchronized precise labels of brushing surface timings and moments of transition. We annotate 10,230 instances of brushing on different surfaces from 114 episodes and evaluate the impact of wide between-person and within-person between-episode variability on machine learning model's performance for brushing surface detection. 

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5. Automated Detection of Stressful Conversations Using Wearable Physiological and Inertial Sensors

   https://doi.org/10.1145/3432210  

   Bari, Rummana; Rahman, Md Mahbubur; Saleheen, Nazir; Parsons, Megan Battles; Buder, Eugene H.; Kumar, Santosh (December 2020, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Stressful conversation is a frequently occurring stressor in our daily life. Stressors not only adversely affect our physical and mental health but also our relationships with family, friends, and coworkers. In this paper, we present a model to automatically detect stressful conversations using wearable physiological and inertial sensors. We conducted a lab and a field study with cohabiting couples to collect ecologically valid sensor data with temporally-precise labels of stressors. We introduce the concept of stress cycles, i.e., the physiological arousal and recovery, within a stress event. We identify several novel features from stress cycles and show that they exhibit distinguishing patterns during stressful conversations when compared to physiological response due to other stressors. We observe that hand gestures also show a distinct pattern when stress occurs due to stressful conversations. We train and test our model using field data collected from 38 participants. Our model can determine whether a detected stress event is due to a stressful conversation with an F1-score of 0.83, using features obtained from only one stress cycle, facilitating intervention delivery within 3.9 minutes since the start of a stressful conversation. 

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