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1. Stress Detection via Sensor Translation

   https://doi.org/10.1109/DCOSS49796.2020.00017  

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

   Stress increases the risk of several mental and physical health problems like anxiety, hypertension, and cardiovascular diseases. Better guidance and interventions towards mitigating the impact of stress can be provided if stress can be monitored continuously. The recent proliferation of wearable devices and their capability in measuring several physiological signals related to stress have created the opportunity to measure stress continuously in the wild. Wearable devices used to measure physiological signals are mostly placed on the wrist and the chest. Though currently chest sensors, with/without wrist sensors, provide better results in detecting stress than using wrist sensors only, chest devices are not as convenient and prevalent as wrist devices, particularly in the free-living context. In this paper, we present a solution to detect stress using wrist sensors that emulate the gold standard chest sensors. Data from wrist sensors are translated into the data from chest sensors, and the translated data is used for stress detection without requiring the users to wear any device on the chest. We evaluated our solution using a public dataset, and results show that our solution detects stress with accuracy comparable to the gold standard chest devices which are impractical for daily use 

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2. Innovating Medication Adherence for Smart Cities: Leveraging PERACTIV and Automated Annotation Pipeline for Innovative Solutions

   https://doi.org/10.1007/978-3-031-82475-3_10  

   Kakaraparthi, Vishnu; Shetty, Yatiraj; Goldberg, Morris; McDaniel, Troy (March 2025, Springer Nature Switzerland)

   The prevalence of medication non-adherence poses a critical challenge with far-reaching health implications and substantial economic consequences. The PERACTIV project addresses this issue with a wrist-worn device that meticulously tracks hand and finger movements, offering real-time insights into medication intake routines. In the context of smart cities and healthcare innovation, this project provides a groundbreaking solution to the persistent problem of medication adherence. This paper introduces the integration of PERACTIV wearable technology with an automated annotation pipeline utilizing Force Sensing Resistor (FSR) technology. Placed strategically on the index finger, FSR records force during pivotal pill-related moments, eliminating the need for time-consuming manual data annotation. Together, the PERACTIV wearable and FSR-powered pipeline form a promising solution for medication adherence, particularly beneficial for individuals living independently and those with memory impairments. The research methodology involved focus groups to explore the intricacies of medication adherence challenges. Section 2 discusses the findings derived from these groups, providing a foundational understanding. Section 3 introduces the FSR-powered automated annotation pipeline, comprehensively analyzing micro-activities during pill-taking. These micro-activities encompass diverse elements, contributing to a holistic understanding crucial for effective analysis. This paper underscores the transformative potential of technology and innovation in healthcare research, setting the stage for seamless integration of medication adherence into daily life. The future promises a more connected and informed healthcare landscape driven by the synergy of PERACTIV wearable and FSR automation. 

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3. Thermally Drawn Stretchable Electrical and Optical Fiber Sensors for Multimodal Extreme Deformation Sensing

   https://doi.org/10.1002/adom.202001815  

   Zhang, Yujing; Li, Xiyuan; Kim, Jongwoon; Tong, Yuxin; Thompson, Emily_G; Jiang, Shan; Feng, Ziang; Yu, Li; Wang, Jinhua; Ha, Dong_Sam; et al (January 2021, Advanced Optical Materials)

   Abstract Highly stretchable fiber sensors have attracted significant interest recently due to their applications in wearable electronics, human–machine interfaces, and biomedical implantable devices. Here, a scalable approach for fabricating stretchable multifunctional electrical and optical fiber sensors using a thermal drawing process is reported. The fiber sensors can sustain at least 580% strain and up to 750% strain with a helix structure. The electrical fiber sensor simultaneously exhibits ultrahigh stretchability (400%), high gauge factors (≈1960), and excellent durability during 1000 stretching and bending cycles. It is also shown that the stretchable step‐index optical fibers facilitate detection of bending and stretching deformation through changes in the light transmission. By combining both electrical and optical detection schemes, multifunctional fibers can be used for quantifying and distinguishing multimodal deformations such as bending and stretching. The fibers’ utility and functionality in sensing and control applications are demonstrated in a smart glove for controlling a virtual hand model, a wrist brace for wrist motion tracking, fiber meshes for strain mapping, and real‐time monitoring of multiaxial expansion and shrinkage of porcine bladders. These results demonstrate that the fiber sensors can be promising candidates for smart textiles, robotics, prosthetics, and biomedical implantable devices. 

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4. w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices

   https://doi.org/10.3390/s20185356  

   Bhat, Ganapati; Tran, Nicholas; Shill, Holly; Ogras, Umit Y. (September 2020, Sensors)

   null (Ed.)

   Human activity recognition (HAR) is growing in popularity due to its wide-ranging applications in patient rehabilitation and movement disorders. HAR approaches typically start with collecting sensor data for the activities under consideration and then develop algorithms using the dataset. As such, the success of algorithms for HAR depends on the availability and quality of datasets. Most of the existing work on HAR uses data from inertial sensors on wearable devices or smartphones to design HAR algorithms. However, inertial sensors exhibit high noise that makes it difficult to segment the data and classify the activities. Furthermore, existing approaches typically do not make their data available publicly, which makes it difficult or impossible to obtain comparisons of HAR approaches. To address these issues, we present wearable HAR (w-HAR) which contains labeled data of seven activities from 22 users. Our dataset’s unique aspect is the integration of data from inertial and wearable stretch sensors, thus providing two modalities of activity information. The wearable stretch sensor data allows us to create variable-length segment data and ensure that each segment contains a single activity. We also provide a HAR framework to use w-HAR to classify the activities. To this end, we first perform a design space exploration to choose a neural network architecture for activity classification. Then, we use two online learning algorithms to adapt the classifier to users whose data are not included at design time. Experiments on the w-HAR dataset show that our framework achieves 95% accuracy while the online learning algorithms improve the accuracy by as much as 40%. 

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5. 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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