Md Abu Sayeed Mondol
ABSTRACT
Activity recognition using wearable sensors has widespread and important applications in different domains including healthcare, safety and behavior monitoring. Almost all the solutions for gesture and activity recognition are data-driven, and so, understanding the data and their characteristics is fundamental toward developing effective solutions. Visualization is perhaps the most effective approach for getting insight into data as well as communicating the insights with others. A novel visualization method that would provide additional utility to the existing methods is of utmost desire. However, such novel methods for visualization are rarely invented, particularly in the area of wearable and mobile sensing. This paper presents novel methods for visualizing movement and orientation using inertial sensors. we demonstrate the use of the methods for visualizing several activities and gestures. We also developed an efficient method for smoking puff detection leveraging the visualization methods.
- 2020. Rehabilitation Exercises. https://orthoinfo.aaos.org/globalassets/pdfs/2017-rehab_shoulder.pdf. (2020). Last accessed: 2020-08-29.Google Scholar
- Fayez Alharbi and Katayoun Farrahi. 2018. A Convolutional Neural Network for Smoking Activity Recognition. In 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom). IEEE, 1–6.Google ScholarCross Ref
- Jonathan Alon, Vassilis Athitsos, Quan Yuan, and Stan Sclaroff. 2005. Simultaneous localization and recognition of dynamic hand gestures. In Application of Computer Vision, 2005. WACV/MOTIONS’05 Volume 1. Seventh IEEE Workshops on, Vol. 2. IEEE, 254–260.Google ScholarDigital Library
- Luyang Liu, Cagdas Karatas, Hongyu Li, Sheng Tan, Marco Gruteser, Jie Yang, Yingying Chen, and Richard P Martin. 2015. Toward detection of unsafe driving with wearables. In Proceedings of the 2015 workshop on Wearable Systems and Applications. ACM, 27–32.Google ScholarDigital Library
- Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. J. of machine learning research 9, Nov (2008), 2579–2605.Google Scholar
- Detlev Majewski, Dörte Liermann, Peter Prohl, Bodo Ritter, Michael Buchhold, Thomas Hanisch, Gerhard Paul, Werner Wergen, and John Baumgardner. 1998. The new global icosahedral–hexagonal grid point model GME of the Deutscher Wetterdienst. In Proc. ECMWF Seminar on Recent Developments in Numerical Methods for Atmospheric Modelling, ECMWF, Reading, United Kingdom. Citeseer.Google Scholar
- Detlev Majewski, Dörte Liermann, Peter Prohl, Bodo Ritter, Michael Buchhold, Thomas Hanisch, Gerhard Paul, Werner Wergen, and John Baumgardner. 2002. The operational global icosahedral–hexagonal gridpoint model GME: description and high-resolution tests. Monthly Weather Review 130, 2 (2002), 319–338.Google ScholarCross Ref
- Md Abu Sayeed Mondol, Brooke Bell, Meiyi Ma, Ridwan Alam, Ifat Emi, Sarah Masud Preum, Kayla de la Haye, Donna Spruijt-Metz, John C Lach, and John A Stankovic. 2020. MFED: A System for Monitoring Family Eating Dynamics. arXiv preprint arXiv:2007.05831(2020).Google Scholar
- Md Abu Sayeed Mondol, Ifat Afrin Emi, and John A Stankovic. 2016. MedRem: an interactive medication reminder and tracking system on wrist devices. In Wireless Health. IEEE, 1–8.Google Scholar
- Md Abu Sayeed Mondol and John A Stankovic. 2020. HAWAD: Hand Washing Detection using Wrist Wearable Inertial Sensors. In The 16th International Conference on Distributed Computing in Sensor Systems.Google Scholar
- Abhinav Parate, Meng-Chieh Chiu, Chaniel Chadowitz, Deepak Ganesan, and Evangelos Kalogerakis. 2014. Risq: Recognizing smoking gestures with inertial sensors on a wristband. In Proceedings of the 12th annual international conference on Mobile systems, applications, and services. ACM, 149–161.Google ScholarDigital Library
- Didier Pittet, Benedetta Allegranzi, and John Boyce. 2009. The World Health Organization guidelines on hand hygiene in health care and their consensus recommendations. Infection Control & Hospital Epidemiology 30, 07 (2009), 611–622.Google ScholarCross Ref
- Muhammad Shoaib, Hans Scholten, Paul JM Havinga, and Ozlem Durmaz Incel. 2016. A hierarchical lazy smoking detection algorithm using smartwatch sensors. In 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom). IEEE.Google ScholarCross Ref
- James P Zacny and Maxine L Stitzer. 1996. Human smoking patterns. Smoking and tobacco control monograph7 (1996), 151–60.Google Scholar
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