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

Samyoun, Sirat; Shubha, Sudipta Saha; Mondol, Abu Sayeed; Stankovic, John A.

doi:10.1016/j.smhl.2020.100171

Cited by 22 publications

(13 citation statements)

References 24 publications

(43 reference statements)

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“…Four types of machine learning (ML) models were investigated as they were applied in the previous works: SVM [10, 22], K‐Nearest Neighborhood (KNN) [16], XGBoost [22], and FFN [18]. The SVM model applied the Gaussian kernel and was trained in the one‐versus‐one mode.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, it is advisable to provide individuals with feedback, especially while helping them form the hand washing habit. However, these sensors cannot identify different users unless assisted with other sensors, for example, Beacon or motion sensors [ 16 ]. Despite being able to recognise people, cameras cause privacy concerns, especially when installed in the bathroom.…”

Section: Introductionmentioning

confidence: 99%

“…The number of sessions used for training the model was examined in the user‐dependent mode, which indicated that after using the data of 16 sessions, the classification accuracy could reach 90%. In study [ 16 ], an online monitoring system including a smartwatch was developed with the Convolutional (CNN) combined with Long Short Term Memory (LSTM) neural network as the classifier. The accuracy of each step was over 90% in the real‐life dataset.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Zhang

Xue

Liu

et al. 2021

Healthc. technol. lett.

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Zhang

Xue

Liu

et al. 2021

Healthc. technol. lett.

View full text Add to dashboard Cite

show abstract

“…For example, Sudharsan et al [32] continuously monitored human arm and hand movements using a wearable device to warn device users before their hands touched their faces. In addition, iWash [33], a smartwatch handwash system, was developed to remind users of washing their hands when they come back home. Studies have collected smartwatch data over time to detect/predict COVID-19 cases.…”

Section: Smartwatch Use For Combating Covid-19mentioning

confidence: 99%

From Small to Big: Smartwatch Use in Mitigating COVID-19 – Understanding User Experience from Social Media Content Analysis

Lai¹,

Zhou²,

Wang³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Smartwatches offer both functions and convenience that can have great potentials for technological interventions. Despite widespread discussion of technological interventions for COVID-19, smartwatch use has received little attention in the literature. This research aims to fill the literature gap by providing a broad understanding of smartwatch use for COVID-19 mitigation. We investigate smartwatch use through content analysis of the data collected from two social media platforms. The method allows us to draw on user experience beyond technological features and functions.In addition to functions, we also identified the concerns of using smartwatches for mitigating COVID-19. Furthermore, we uncovered both similarities and differences between the different social media platforms in terms of functions and concerns of smartwatch use. Our findings have implications for various stakeholders of the smartwatch technology and for mitigating the impact of the pandemic.

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“…There has been a rampant spread of misinformation related to oxygen, availability of oxygen beds, vaccines, drugs, and many other things. For authentication and validation of such information web search engines have been created 10 , various IoT and AI-based tools have been created to raise awareness for concerns related to handwashing, social hygiene, maintaining social distancing, and wearing masks 11 , 12 .…”

Section: Introductionmentioning

confidence: 99%

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

Pandey

Gautam

Pal

et al. 2022

Sci Rep

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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.

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

Cited by 22 publications

References 24 publications

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

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

From Small to Big: Smartwatch Use in Mitigating COVID-19 – Understanding User Experience from Social Media Content Analysis

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

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