Top

Published in:

01-12-2015 | Systems-Level Quality Improvement

An Adaptive Sensor Data Segments Selection Method for Wearable Health Care Services

Authors: Shih-Yeh Chen, Chin-Feng Lai, Ren-Hung Hwang, Ying-Hsun Lai, Ming-Shi Wang

Published in: Journal of Medical Systems | Issue 12/2015

Abstract

As cloud computing and wearable devices technologies mature, relevant services have grown more and more popular in recent years. The healthcare field is one of the popular services for this technology that adopts wearable devices to sense signals of negative physiological events, and to notify users. The development and implementation of long-term healthcare monitoring that can prevent or quickly respond to the occurrence of disease and accidents present an interesting challenge for computing power and energy limits. This study proposed an adaptive sensor data segments selection method for wearable health care services, and considered the sensing frequency of the various signals from human body, as well as the data transmission among the devices. The healthcare service regulates the sensing frequency of devices by considering the overall cloud computing environment and the sensing variations of wearable health care services. The experimental results show that the proposed service can effectively transmit the sensing data and prolong the overall lifetime of health care services.

Zhangjie, F., Sun, X., Qi, L., Zhou, L., and Shu, J., Achieving Efficient Cloud Search Services: Multi-keyword Ranked Search over Encrypted Cloud Data Supporting Parallel Computing. IEICE Trans. Comm. E98-B(1):190–200, 2015.CrossRef

Ren, Y., Shen, J., Wang, J., Han, J., and Lee, S., Mutual Verifiable Provable Data Auditing in Public Cloud Storage. Journal of Internet Technology 16(2):317–323, 2015.

Zhihua Xia, Xinhui Wang, Xingming Sun, and Qian Wang, A Secure and Dynamic Multi-keyword Ranked Search Scheme over Encrypted Cloud Data. IEEE Trans. Parallel Distrib. Syst., 2015. doi:10.1109/TPDS.2015.2401003.

Zhou, Y., and Zhu, J., Design and implementation of Zig-bee based wireless sensor network for remote SpO2 monitor. In: Proc. of 2010 2nd International Conference on Future Computer and Communication (ICFCC), pp. 278–281, 2010.

Adochiei, F., Rotariu, C., Ciobotariu, R., and Costin, H., wireless low-power pulse oximetry system for patient telemonitoring. In: Proc Of 2011 7th International Symposium on Advanced Topics in Electrical Engineering (ATEE), pp. 1–4, 2011.

Naraharisetti, K. V. P., and Bawa,M., Comparison of different signal processing methods for reducing artifacts from photoplethysmograph signal. In: Proc Of 2011 IEEE International Conference on Electro/Information Technology (EIT), pp. 1–8, 2011.

Otero, A., Felix, P., Presedo, J., and Zamarron, C., Evaluation of an alternative definition for the apnea-hypopnea index. In: Proc Of 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4654–4657, 2010.

Nobuyuki, A., Yasuhiro, N., Taiki, T., Miyae, Y., Kiyoko, M., and Terumasa, H., Trial of Measurement of Sleep Apnea Syndrome with SoundMonitoring and SpO2 at home. In: Proc. Of 11th International Conference on e-Health Networking, Applications and Services, pp. 66–69, 2010.

Ranjan Singh, R., and Banerjee, R., Multi-parametric analysis of sensory data collected from automotive drivers for building a safety-critical wearable computing system. In: Proc Of 2010 2nd International Conference on Computer Engineering and Technology (ICCET), pp. 355–360, 2010.

10.

Ghasemzadeh, H., Loseu, V., and Jafari, R., Structural Action Recognition in Body Sensor Networks: Distributed Classification Based on String Matching. IEEE Trans. Inf. Technol. Biomed. 14(2):425–435, 2010.CrossRefPubMed

11.

Hong, Y.-J., Kim, I.-J., Chul Ahn, S., and Kim, H.-G.: Activity Recognition Using Wearable Sensors for Elder Care, 2008.

12.

Cao, H., Leung, V., Chow, C., and Chan, H., Enabling Technologies for Wireless Body Area Networks: A Survey and Outlook. IEEE Commun. Mag. 47(12):84–93, 2009.CrossRef

13.

Chung, W.-Y., Lee, Y.-D., and Jung, S.-J., A Wireless Sensor Network Compatible Wearable U-healthcare Monitoring System Using Integrated ECG, Accelerometer and SpO2, pp. 1529–1532, 2008.

14.

Lee, H., Lim, S., and Huh, J., Design and Implementation of Baby-care Service based on Context-awareness for Digital Home. In: The 7th IEEE International Conference on Advanced Communication Technology, Vol. 11, pp. 908–911, 2005.

15.

Huo, H., Xu, Y., Yan, H., Mubeen, S., and Zhang, H., An Elderly Health Care System Using Wireless Sensor Networks at Home. In: Sensor Third International Conference on Technologies and Applications, pp. 158–163, 2009.

16.

Tang, Y., Wang, S., Chen, Y., and Chen, Z., PPCare: A Personal and Pervasive Health Care System for the Elderly. In: 9th International Conference on Ubiquitous Intelligence & Computing and 9th International Conference on Autonomic & Trusted Computing (UIC/ATC), 2012.

17.

Hossain, M. A., and Ahmed, D. T., Virtual Caregiver: An Ambient-Aware Elderly Monitoring System. IEEE Trans. Inf. Technol. Biomed. 16(6):1024–1031, 2012.CrossRefPubMed

18.

Bourke, A. K., OBrien, J. V., and Lyons, G. M., Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait & Posture 26:194–199, 2007.CrossRef

19.

Dai, J., Bai, X., Yang, Z., Shen, Z., and Xuan, D., PerFallD: A pervasive fall detection system using mobile phones. In: IEEE International Conference on Pervasive Computing and Communications Workshops(PERCOM Workshops), pp. 292–297, 2010.

20.

Sposaro, F., and Tyson, G., iFall: An Android Application for Fall Monitoring and Response. In: 31st Annual International Conference of the IEEE EMBS, pp. 6119–6122, 2009.

21.

Nyan, M. N., Tay, F. E. H., and Tan, A.W. Y., Seah Distinguishing Fall Activities from Normal Activities by Angular Rate Characteristics and High-Speed Camera Characterization. Med. Eng. Phys. 28(8):842–849, 2006.CrossRefPubMed

22.

Lai, C.-F., Chang, S.-Y., Chao, H.-C., and Huang, Y.-M., Detection of Cognitive Injured Body Region UsingMultiple Triaxial Accelerometers for Elderly Falling. IEEE Sensors J. 11(3):763–770, 2011.CrossRef

23.

Chang, S.-Y., C-F Lai, H.-C., Chao, J., and Park, Y.-M., Huang, An Environmental-Adaptive Fall detection System on Mobile Device. J. Med. Syst. 35(5):1299–1312, 2011.CrossRefPubMed

24.

Lai, C.-F., Huang, Y.-M., Park, J., and Chao, H.-C., Adaptive Body Posture Analysis Using Collaborative Multi-Sensors for Elderly Falling Detection. IEEE Intell. Syst. 24(6):20–30, 2010.

25.

Degen, T., Jaeckel, H., Rufer, M., and Wyss, S., SPEEDY: a fall detector in a wrist watch. In: Proc. Seventh IEEE Int. Symp. on Wearable Computers (ISWC’03), New-York, pp. 184–187, (2003)

26.

Chen, D., Zhang, Y., Feng, W., and X. Li: A wireless realtime fall detecting system based on barometer and accelerometer (2012).

27.

Krunz, M., Muqattash, A., and Lee, S.-J., Transmission power control in wireless ad hoc networks: challenges, solutions and open issues. Network 18(5):8–14, 2004.

28.

Correia Luiz, H. A., Macedo Daniel, F., dos Santos Aldri, L., Loureiro Antonio, A. F., Marcos, S., and Nogueira, J., Transmission power control techniques for wireless sensor networks. Comput. Netw. 4765–4779, 2007.

29.

Lin, S., Zhang, J., Zhou, G., Gu, L., He, T., and Stankovic John, A., ATPC: Adaptive Transmission Power Control for Wireless Sensor Networks. In: Proceedings of the 4th international conference on Embedded networked sensor systems, pp. 223–236, 2006.

30.

Egwar, A. A., and Rai, I. A., Power Aware Differentiated Routing (PADR) in Wireless Sensor Networks. In: Proceedings of 18th Internatonal Conference on Computer Communications and Networks, Vol. 3-6, pp. 1–6, 2009.

31.

Egwar, A. A.: Power Aware Differentiated Routing(PADR) in Wireless Sensor Networks, Master thesis in Data Communication and Software Engineering of Makerere University, 2009.

32.

Wood, A., Stankovic, J. A., Virone, G., Selavo, L., He, Z., Cao, Q., Doan, T., Wu, Y., Fang, L., and Stoleru, R., Context-Aware Wireless Sensor Networks for Assisted Living and Residential Monitoring. IEEE Netw. 22(4):26–33, 2008.

33.

Nishihara, K., Ishizaka, K., and Sakai, J., Power Saving in Mobile Devices Using Context-Aware Resource Control. In: First International Conference on Networking and Computing, pp. 220–226, 2010.

Title: An Adaptive Sensor Data Segments Selection Method for Wearable Health Care Services
Authors: Shih-Yeh Chen
Chin-Feng Lai
Ren-Hung Hwang
Ying-Hsun Lai
Ming-Shi Wang
Publication date: 01-12-2015
Publisher: Springer US
Published in: Journal of Medical Systems / Issue 12/2015
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-015-0343-y

2024 ASCO Annual Meeting

Springer Medicine

An Adaptive Sensor Data Segments Selection Method for Wearable Health Care Services

Abstract

2024 ASCO Annual Meeting

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 12/2015

A Fatigue Measuring Protocol for Wireless Body Area Sensor Networks

The Effect of Electrode Designs Based on the Anatomical Heart Location for the Non-Contact Heart Activity Measurement

iMStrong: Deployment of a Biosensor System to Detect Cocaine Use

A Semantic Big Data Platform for Integrating Heterogeneous Wearable Data in Healthcare

Integration of Wearable Solutions in AAL Environments with Mobility Support

Toward a Fault Tolerant Architecture for Vital Medical-Based Wearable Computing