최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기International journal of big data and analytics in healthcare, v.4 no.1, 2019년, pp.32 - 46
Prayaga, Lakshmi (University of West Florida, Pensacola, USA) , Devulapalli, Krishna (Indian Institute of Chemical Technology, Secunderabad, India) , Prayaga, Chandra (University of West Florida, Pensacola, USA)
Wearable devices are contributing heavily towards the proliferation of data and creating a rich minefield for data analytics. Recent trends in the design of wearable devices include several embedded sensors which also provide useful data for many applications. This research presents results obtained...
Ahamed, Nizam Uddin, Kobsar, Dylan, Benson, Lauren, Clermont, Christian, Kohrs, Russell, Osis, Sean T., Ferber, Reed. Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions. PloS one, vol.13, no.9, e0203839-.
Automation in Construction R.Akhavian 2016 Smartphone-based construction workers’ activity recognition and classification
10.1007/s11257-014-9146-y op den Akker, H., Jones, V. M., & Hermens, H. J. (2014). Tailoring real-time physical activity coaching systems: a literature survey and model. User modeling and user-adapted interaction, 24(5), 351-392.
Balli, Serkan, Sağbaş, Ensar Arif, Peker, Musa. Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm. Measurement and control, vol.52, no.1, 37-45.
Cadmus-Bertram, L.. Using Fitness Trackers in Clinical Research: What Nurse Practitioners Need to Know. The journal for nurse practitioners : JNP, vol.13, no.1, 34-40.
Cammarota, A. (2003), “The commission’s initiative on MSDS: Recent developments in social partner consultation at the European level. Presented at the Conference on MSDs - A Challenge for the Telecommunications Industry, Lisbon, Portugal, October 20-21 (pp. 20-21).
del Rosario, Michael B., Redmond, Stephen J., Lovell, Nigel H.. Tracking the Evolution of Smartphone Sensing for Monitoring Human Movement. Sensors, vol.15, no.8, 18901-18933.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction T.Hastie 2016
Henriksen, André, Haugen Mikalsen, Martin, Woldaregay, Ashenafi Zebene, Muzny, Miroslav, Hartvigsen, Gunnar, Hopstock, Laila Arnesdatter, Grimsgaard, Sameline. Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables. Journal of medical Internet research, vol.20, no.3, e110-.
Henriksen, André, Haugen Mikalsen, Martin, Woldaregay, Ashenafi Zebene, Muzny, Miroslav, Hartvigsen, Gunnar, Hopstock, Laila Arnesdatter, Grimsgaard, Sameline. Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables. Journal of medical Internet research, vol.20, no.3, e110-.
Procedia Computer Science 113 202 2017 10.1016/j.procs.2017.08.349 Identifying Smartphone Users based on their Activity Patterns via Mobile Sensing.
Maher, Carol, Ryan, Jillian, Ambrosi, Christina, Edney, Sarah. Users’ experiences of wearable activity trackers: a cross-sectional study. BMC public health, vol.17, no.1, 880-.
International Journal of Intelligent Systems Technologies and Applications B.Natarajan 2017 Empirical study of feature selection methods over classification algorithms
Pierluigi, C., Oriol, P., & Petia, R. (2011), Human Activity Recognition from Accelerometer Data Using a Wearable Device. In Pattern Recognition and Image Analysis:5th Iberian Conference, IbPRIA 2011, Las Palmas de Gran Canaria, Spain, June 8-10 (pp. 289-296).
Ridgers, Nicola D, Timperio, Anna, Brown, Helen, Ball, Kylie, Macfarlane, Susie, Lai, Samuel K, Richards, Kara, Mackintosh, Kelly A, McNarry, Melitta A, Foster, Megan, Salmon, Jo. Wearable Activity Tracker Use Among Australian Adolescents: Usability and Acceptability Study. JMIR mHealth and uHealth, vol.6, no.4, e86-.
McGinnis, R. S., DiCristofaro, S., Mahadevan, N., Sen-Gupta, E., Silva, I., Jortberg, E., ... & Patel, S. (2017, August). Longitudinal Data from Wearable Sensor System Suggests Movement Improves Standing Posture. In Proceedings of the 41st Annual Meeting of the American Society of Biomechanics, Boulder, CO (pp. 8-11).
Tillis, R. (2016). Machine Learning Project - random forest - Sensor Data. RPubs.
Information Fusion S.Zhang 41 37 2018 10.1016/j.inffus.2017.08.003 I sense overeating: Motif-based machine learning framework to detect overeating using wrist-worn sensing.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.