iWash: A smartwatch handwashing quality assessment and reminder system with real-time feedback in the context of infectious disease

Exploring the Impact of the NULL Class on In-the-Wild Human Activity Recognition

Monitoring activities of daily living (ADLs) plays an important role in measuring and responding to a person's ability to manage their basic physical needs. Effective recognition systems for monitoring ADLs must successfully recognize naturalistic activities that also realistically occur at infrequent intervals. However, existing systems primarily focus on either recognizing more separable, controlled activity types or are trained on balanced datasets where activities occur more frequently. In our work, we investigate the challenges associated with applying machine learning to an imbalanced dataset collected from a fully in-the-wild environment. This analysis shows that the combination of preprocessing techniques to increase recall and postprocessing techniques to increase precision can result in more desirable models for tasks such as ADL monitoring. In a user-independent evaluation using in-the-wild data, these techniques resulted in a model that achieved an event-based F1-score of over 0.9 for brushing teeth, combing hair, walking, and washing hands. This work tackles fundamental challenges in machine learning that will need to be addressed in order for these systems to be deployed and reliably work in the real world.

Context-Aware Edge-Based AI Models for Wireless Sensor Networks-An Overview

Recent advances in sensor technology are expected to lead to a greater use of wireless sensor networks (WSNs) in industry, logistics, healthcare, etc. On the other hand, advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are becoming dominant solutions for processing large amounts of data from edge-synthesized heterogeneous sensors and drawing accurate conclusions with better understanding of the situation. Integration of the two areas WSN and AI has resulted in more accurate measurements, context-aware analysis and prediction useful for smart sensing applications. In this paper, a comprehensive overview of the latest developments in context-aware intelligent systems using sensor technology is provided. In addition, it also discusses the areas in which they are used, related challenges, motivations for adopting AI solutions, focusing on edge computing, i.e., sensor and AI techniques, along with analysis of existing research gaps. Another contribution of this study is the use of a semantic-aware approach to extract survey-relevant subjects. The latter specifically identifies eleven main research topics supported by the articles included in the work. These are analyzed from various angles to answer five main research questions. Finally, potential future research directions are also discussed.

VoiceCare: A Voice-Interactive Cognitive Assistant on a Smartwatch for Monitoring and Assisting Daily Healthcare Activities

Following several health activities in daily life (e.g., medication/exercise plans, handwashing, physiological monitoring) properly often requires monitoring and assistance support. Although the emergent smartwatch and wearable technologies have opened great opportunities to monitor these activities in the wild, existing smartwatch-based systems do not interactively guide the user and also lacks comprehensiveness to provide knowledge related to this set of daily activities. To overcome these limitations of the state-of-the-art, we present Voice Care, a wearable cognitive assistant on a smartwatch for daily life healthcare that interactively assists the user and monitors adherence to these activities. We conduct an extensive user study and thorough evaluation on collected data to show that VoiceCare effectively brings multiple health sensing solutions into one system while operating with minimal resource usage and helps improve the user conformance to daily health.

A machine learning application for raising WASH awareness in the times of COVID-19 pandemic

The COVID-19 pandemic has revealed the power of internet disinformation in influencing global health. The deluge of information travels faster than the epidemic itself and is a threat to the health of millions across the globe. Health apps need to leverage machine learning for delivering the right information while constantly learning misinformation trends and deliver these effectively in vernacular languages in order to combat the infodemic at the grassroot levels in the general public. Our application, WashKaro, is a multi-pronged intervention that uses conversational Artificial Intelligence (AI), machine translation, and natural language processing to combat misinformation (NLP). WashKaro uses AI to provide accurate information matched against WHO recommendations and delivered in an understandable format in local languages. The primary aim of this study was to assess the use of neural models for text summarization and machine learning for delivering WHO matched COVID-19 information to mitigate the misinfodemic. The secondary aim of this study was to develop a symptom assessment tool and segmentation insights for improving the delivery of information. A total of 5026 people downloaded the app during the study window; among those, 1545 were actively engaged users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot "Satya" increased thus proving the usefulness of a mHealth platform to mitigate health misinformation. We conclude that a machine learning application delivering bite-sized vernacular audios and conversational AI is a practical approach to mitigate health misinformation.

Detecting hand washing activity among activities of daily living and classification of WHO hand washing techniques using wearable devices and machine learning algorithms

During COVID-19, awareness of proper hand washing has increased significantly. It is critical that people learn the correct hand washing techniques and adopt good hand washing habits. Hence, this study proposes using wearable devices to detect hand washing activity among other daily living activities (ADLs) and classify steps proposed by the World Health Organization (WHO). Two experiments were conducted with 16 participants, aged from 20 to 31. The first experiment was hand washing following WHO regulation (ten participants), and the second experiment was performing eight ADLs (eight participants). All participants wore two wearable devices equipped with accelerometers and gyroscopes; one on each wrist. Four machine learning classifiers were compared in classifying hand washing steps in the leave-one-subject-out (LOSO) mode. The SVM model with Gaussian kernel achieved the best performance in classifying 11 washing hands steps, with an average F1-score of 0.8501. When detected among the other ADLs, hand washing following WHO regulation obtained the F1-score of 0.9871. The study demonstrates that wearable devices are feasible to detect hand washing activity and the hand washing techniques as well. The classification results of getting the soap and rubbing thumbs are low, which will be the main focus in the future study.

Sticky apps, not sticky hands: A systematic review and content synthesis of hand hygiene mobile apps

OBJECTIVE

The study sought to identify smartphone apps that support hand hygiene practice and to assess their content, technical and functional features, and quality. A secondary objective was to make design and research recommendations for future apps.

MATERIALS AND METHODS

We searched the UK Google Play and Apple App stores for hand hygiene smartphone apps aimed at adults. Information regarding content, technical and functional features was extracted and summarized. Two raters evaluated each app, using the IMS Institute for Healthcare Informatics functionality score and the Mobile App Rating Scale (MARS).

RESULTS

A total of 668 apps were identified, with 90 meeting the inclusion criteria. Most (96%) were free to download. The majority (78%) intended to educate or inform or remind users to hand wash (69%), using behavior change techniques such as personalization and prompting practice. Only 20% and 4% named a best practice guideline or had expert involvement in development, respectively. Innovative means of engagement were used in 42% (eg, virtual or augmented reality or geolocation-based reminders). Apps included an average of 2.4 out of 10 of the IMS functionality criteria (range, 0-8). The mean MARS score was 3.2 ± 0.5 out of 5, and 68% had a minimum acceptability score of 3. Two had been tested or trialed.

CONCLUSIONS

Although many hand hygiene apps exist, few provide content on best practice. Many did not meet the minimum acceptability criterion for quality or were formally trialed or tested. Research should assess the feasibility and effectiveness of hand hygiene apps (especially within healthcare settings), including when and how they "work." We recommend that future apps to support hand hygiene practice are developed with infection prevention and control experts and align with best practice. Robust research is needed to determine which innovative methods of engagement create "sticky" apps.