A Framework for the Development of Computing-Assisted Health Surveillance among Drug Dependents in Davao City
Pages 140 - 145
Abstract
With the ongoing efforts of the Philippine Government in addressing drug-related problems in the society, issues like lack of healthcare service providers and medical facilities to offer rehabilitation programs has been detrimental to the success of these efforts. In this paper, we present a framework for the development of a health surveillance which takes advantage of the potential of inertial sensors in smartphones as well as other wearable devices in capturing physical and physiological data. This data is then analyzed in order to assess the rehabilitation progress and the overall rehabilitation outcome of drug-dependent individuals. The framework is composed of three main parts. First is sensing which covers the collection as well as pre-processing of data. Second is analysis which implements an ANN-based approach for the classification of physical activities. Lastly is results generation which provides healthcare service providers a means to better understand the data of individuals and assess their rehabilitation outcomes. Future works include the use of fewer bodily-sensors while collecting the same amount of data as well as providing visualization of the data analysis results.
References
[1]
World Drug Report 2018. 2019. Unodc.org. https://www.unodc.org/wdr2018/.
[2]
Rengert, G. The Geography of Illegal Drugs.
[3]
Cousins, S. 2016. Five thousand dead and counting: the Philippines' bloody war on drugs. BMJ, i6177.
[4]
Daley, D. 2013. Family and social aspects of substance use disorders and treatment. Journal of Food and Drug Analysis 21, 4, S73-S76.
[5]
Herradura, I. 2009. Improving the Treatment of Offenders through the Enhancement of Community-Based Alternatives to Incarceration: The Philippine Experience. Unafei.or.jp. https://www.unafei.or.jp/publications/pdf/RSNo79/No7927VEHerradura.pdf.
[6]
Cepeda, M. 2016. DDB wants law institutionalizing community-based drug rehab. Rappler. https://www.rappler.com/nation/147020-ddb-law-institutionalize-community-based-drug-rehabilitation.
[7]
Wager, K. A., Lee, F. W., Glaser, J. P. 2017. Health care information systems: a practical approach for health care management. John Wiley Sons.
[8]
Marcin, J. P., Rimsza, M. E., Moskowitz, W B. 2015. The use of telemedicine to address access and physician workforce shortages. Pediatrics, 136(1), 202--209.
[9]
Thacker, S., Berkelman, R. and Stroup, D. 1989. The Science of Public Health Surveillance. Journal of Public Health Policy. 10, 2 (1989), 187.
[10]
Avci, A., Bosch, S., Marin Perianu, M., Marin Perianu, R., Havinga, P. J. M. 2010. Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey. In 23th International Conference on Architecture of Computing Systems, ARCS 2010 (pp. 167--176). Berlin: VDE Verlag.
[11]
Chen, Y. and Shen, C. 2017. Performance Analysis of Smartphone-Sensor Behavior for Human Activity Recognition. IEEE Access. 5, (2017), 3095--3110.
[12]
Cornacchia, M., Ozcan, K., Zheng, Y. and Velipasalar, S. 2017. A Survey on Activity Detection and Classification Using Wearable Sensors. IEEE Sensors Journal. 17, 2 (2017), 386--403.
[13]
Campbell, A. and Choudhury, T. 2012. From Smart to Cognitive Phones. IEEE Pervasive Computing. 11, 3 (2012), 7--11.
[14]
Tasoulis, S., Doukas, C., Plagianakos, V. and Maglogiannis, I. 2013. Statistical data mining of streaming motion data for activity and fall recognition in assistive environments. Neurocomputing. 107, (2013), 87--96.
[15]
Hassan, M., Uddin, M., Mohamed, A. and Almogren, A. 2018. A robust human activity recognition system using smartphone sensors and deep learning. Future Generation Computer Systems. 81, (2018), 307--313.
[16]
Khan, A., Young-Koo Lee, Lee, S. and Tae-Seong Kim 2010. A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer. IEEE Transactions on Information Technology in Biomedicine. 14, 5 (2010), 1166--1172.
[17]
Maurer, U., Smailagic, A., Siewiorek, D. P., Deisher, M. 2006. Activity recognition and monitoring using multiple sensors on different body positions. CARNEGIE-MELLON UNIV PITTSBURGH PA SCHOOL OF COMPUTER SCIENCE.
[18]
Minnen, D., Starner, T., Ward, J. A., Lukowicz, P., Troster, G. 2005, July. Recognizing and discovering human actions from on-body sensor data. In 2005 IEEE International Conference on Multimedia and Expo (pp. 1545--1548). IEEE.
[19]
Perriot, B., Argod, J., Pepin, J. and Noury, N. 2014. Characterization of Physical Activity in COPD Patients: Validation of a Robust Algorithm for Actigraphic Measurements in Living Situations. IEEE Journal of Biomedical and Health Informatics. 18, 4 (2014), 1225--1231.
[20]
Nam, Y., Rho, S. and Lee, C. 2013. Physical activity recognition using multiple sensors embedded in a wearable device. ACM Transactions on Embedded Computing Systems 12, 2, 1--14.
[21]
Gyllensten, I. and Bonomi, A. 2011. Identifying Types of Physical Activity With a Single Accelerometer: Evaluating Laboratory-trained Algorithms in Daily Life. IEEE Transactions on Biomedical Engineering 58, 9, 2656--2663.
[22]
Ermes, M., Parkka, J., Mantyjarvi, J. and Korhonen, I. 2008. Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions. IEEE Transactions on Information Technology in Biomedicine 12, 1, 20--26.
[23]
Ward, J., Lukowicz, P., Troster, G. and Starner, T. 2006. Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 10, 1553--1567.
[24]
Anguita, D., Ghio, A., Oneto, L., Parra, X. and Reyes-Ortiz, J.L., 2013, September. Training computationally efficient Smartphone-Based human activity recognition models. In International Conference on Artificial Neural Networks (pp. 426--433). Springer, Berlin, Heidelberg.
[25]
Wu, W., Dasgupta, S., Ramirez, E., Peterson, C. and Norman, G. 2012. Classification Accuracies of Physical Activities Using Smartphone Motion Sensors. Journal of Medical Internet Research 14, 5, e130.
[26]
Veltink, P., Bussmann, H., de Vries, W., Martens, W. and Van Lummel, R. 1996. Detection of static and dynamic activities using uniaxial accelerometers. IEEE Transactions on Rehabilitation Engineering 4, 4, 375--385.
[27]
Lyons, G., Culhane, K., Hilton, D., Grace, P. and Lyons, D. 2005. A description of an accelerometer-based mobility monitoring technique. Medical Engineering Physics 27, 6, 497--504.
[28]
Luštrek, M. and Kaluža, B., 2009. Fall detection and activity recognition with machine learning. Informatica, 33(2).
[29]
Paganelli, F. and Giuli, D., 2007, May. An ontology-based context model for home health monitoring and alerting in chronic patient care networks. In 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07) (Vol. 2, pp. 838--845). IEEE.
[30]
Salarian, A., Russmann, H., Wider, C., Burkhard, P., Vingerhoets, F. and Aminian, K. 2007. Quantification of Tremor and Bradykinesia in Parkinson's Disease Using a Novel Ambulatory Monitoring System. IEEE Transactions on Biomedical Engineering 54, 2, 313--322.
[31]
Pappas, I., Popovic, M., Keller, T., Dietz, V. and Morari, M. 2001. A reliable gait phase detection system. IEEE Transactions on Neural Systems and Rehabilitation Engineering 9, 2, 113--125.
[32]
Moncada-Torres, A., Leuenberger, K., Gonzenbach, R., Luft, A. and Gassert, R. 2014. Activity classification based on inertial and barometric pressure sensors at different anatomical locations. Physiological Measurement 35, 7, 1245--1263.
[33]
Bianchi, F., Redmond, S., Narayanan, M., Cerutti, S. and Lovell, N. 2010. Barometric Pressure and Triaxial Accelerometry-Based Falls Event Detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering 18, 6, 619--627.
[34]
Qi, J., Yang, P., Waraich, A., Deng, Z., Zhao, Y. and Yang, Y. 2018. Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review. Journal of Biomedical Informatics 87, 138--153.
Index Terms
- A Framework for the Development of Computing-Assisted Health Surveillance among Drug Dependents in Davao City
Recommendations
Surveillance of methadone-related adverse drug events using multiple public health data sources
Healthcare safety and quality surveillance is increasingly conducted by public health agencies. We describe a biomedical informatics method that uses multiple public health data sources to perform surveillance of methadone-related adverse drug events. ...
Development of Health Care System Based on Smart Clothes
Cross-Cultural Design. Applications in Health, Learning, Communication, and CreativityAbstractWith the rapid growth of the world’s aging population, health care for the elderly has become an important issue in all countries. In order to reduce the manpower burden, reduce medical costs and improve the quality of care services, wearable ...
Facilitating trunk endurance assessment by means of mobile health technologies
UbiComp/ISWC'15 Adjunct: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable ComputersTrunk endurance tests are widely used in physical medicine to assess the muscle status of people affected by low back pain. Nevertheless, traditional evaluation procedures suffer from practical limitations, which can lead to potential misdiagnoses. This ...
Comments
Information & Contributors
Information
Published In
May 2020
163 pages
ISBN:9781450377102
DOI:10.1145/3399871
Copyright © 2020 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- Nanyang Technological University
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 03 July 2020
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ASSE '20
ASSE '20: 2020 Asia Service Sciences and Software Engineering Conference
May 13 - 15, 2020
Nagoya, Japan
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 26Total Downloads
- Downloads (Last 12 months)3
- Downloads (Last 6 weeks)2
Reflects downloads up to 13 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in