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1. PERACTIV: Personalized Activity Monitoring - Ask My Hands

   https://doi.org/10.1007/978-3-031-05431-0_18  

   Kakaraparthi, Vishnu; McDaniel, Troy; Venkateswara, Hemanth; Goldberg, Morris (June 2022, International Conference on Human-Computer Interaction (HCII) 2022. Lecture Notes in Computer Science)

   Medication adherence is a major problem in the healthcare industry: it has a major impact on an individual’s health and is a major expense on the healthcare system. We note that much of human activity involves using our hands, often in conjunction with objects. Camera-based wearables for tracking human activities have sparked a lot of attention in the past few years. These technologies have the potential to track human behavior anytime, any place. This paper proposes a paradigm for medication adherence employing innovative wrist-worn camera technology. We discuss how the device was built, various experiments to demonstrate feasibility and how the device could be deployed to detect the micro-activities involved in pill taking so as to ensure medication adherence. 

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2. An Activity Recognition System for Taking Medicine Using In-The-Wild Data to Promote Medication Adherence

   https://doi.org/10.1145/3397481.3450673  

   Cherian, Josh; Ray, Samantha; Hammond, Tracy (April 2021, IUI '21: 26th International Conference on Intelligent User Interfaces)

   null (Ed.)

   Nearly half of people prescribed medication to treat chronic or short-term conditions do not take their medicine as prescribed. This leads to worse treatment outcomes, higher hospital admission rates, increased healthcare costs, and increased morbidity and mortality rates. While some instances of medication non-adherence are a result of problems with the treatment plan or barriers caused by the health care provider, many are instances caused by patient-related factors such as forgetting, running out of medication, and not understanding the required dosages. This presents a clear need for patient-centered systems that can reliably increase medication adherence. To that end, in this work we describe an activity recognition system capable of recognizing when individuals take medication in an unconstrained, real-world environment. Our methodology uses a modified version of the Bagging ensemble method to suit unbalanced data and a classifier trained on the prediction probabilities of the Bagging classifier to identify when individuals took medication during a full-day study. Using this methodology we are able to recognize when individuals took medication with an F-measure of 0.77. Our system is a first step towards developing personal health interfaces that are capable of providing personalized medication adherence interventions. 

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3. High surface area reverse electrowetting energy harvesting with power conditioning circuitry for self-powered motion sensors

   https://doi.org/10.1117/12.2587541  

   Adhikari, Pashupati R.; Biswas, Dipon K.; Tasneem, Nishat T.; Reid, Russell C.; Mahbub, Ifana (April 2021, SPIE Defense + Commercial Sensing)

   Dutta, Achyut K.; Balaya, Palani; Xu, Sheng (Ed.)

   Monitoring human health in real-time using wearable and implantable electronics (WIE) has become one of the most promising and rapidly growing technologies in the healthcare industry. In general, these electronics are powered by batteries that require periodic replacement and maintenance over their lifetime. To prolong the operation of these electronics, they should have zero post-installation maintenance. On this front, various energy harvesting technologies to generate electrical energy from ambient energy sources have been researched. Many energy harvesters currently available are limited by their power output and energy densities. With the miniaturization of wearable and implantable electronics, the size of the harvesters must be miniaturized accordingly in order to increase the energy density of the harvesters. Additionally, many of the energy harvesters also suffer from limited operational parameters such as resonance frequency and variable input signals. In this work, low frequency motion energy harvesting based on reverse electrowetting-ondielectric (REWOD) is examined using perforated high surface area electrodes with 38 µm pore diameters. Total available surface area per planar area was 8.36 cm2 showing a significant surface area enhancement from planar to porous electrodes and proportional increase in AC voltage density from our previous work. In REWOD energy harvesting, high surface area electrodes significantly increase the capacitance and hence the power density. An AC peak-to-peak voltage generation from the electrode in the range from 1.57-3.32 V for the given frequency range of 1-5 Hz with 0.5 Hz step is demonstrated. In addition, the unconditioned power generated from the harvester is converted to a DC power using a commercial off-theshelf Schottky diode-based voltage multiplier and low dropout regulator (LDO) such that the sensors that use this technology could be fully self-powered. The produced charge is then converted to a proportional voltage by using a commercial charge amplifier to record the features of the motion activities. A transceiver radio is also used to transmit the digitized data from the amplifier and the built-in analog-to-digital converter (ADC) in the micro-controller. This paper proposes the energy harvester acting as a self-powered motion sensor for different physical activities for wearable and wireless healthcare devices. 

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4. Hydrogels in wearable neural interfaces

   https://doi.org/10.1007/s44258-024-00040-4  

   Yao, Mengmeng; Hsieh, Ju-Chun; Tang, Kai_Wing Kevin; Wang, Huiliang (December 2024, Med-X)

   Abstract The integration of wearable neural interfaces (WNIs) with the human nervous system has marked a significant progression, enabling progress in medical treatments and technology integration. Hydrogels, distinguished by their high-water content, low interfacial impedance, conductivity, adhesion, and mechanical compliance, effectively address the rigidity and biocompatibility issues common in traditional materials. This review highlights their important parameters—biocompatibility, interfacial impedance, conductivity, and adhesiveness—that are integral to their function in WNIs. The applications of hydrogels in wearable neural recording and neurostimulation are discussed in detail. Finally, the opportunities and challenges faced by hydrogels for WNIs are summarized and prospected. This review aims to offer a thorough examination of hydrogel technology’s present landscape and to encourage continued exploration and innovation. As developments progress, hydrogels are poised to revolutionize wearable neural interfaces, offering significant enhancements in healthcare and technological applications. Graphical Abstract 

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5. 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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