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InECCE2019 pp 323-332 | Cite as

Design and Development of Wearable Human Activity Recognition for Healthcare Monitoring

  • Hamzah AhmadEmail author
  • Nurul Syafiqah Mohd
  • Nur Aqilah Othman
  • Mohd Mawardi Saari
  • Mohd Syakirin Ramli
Conference paper
  • 26 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 632)

Abstract

This research deals with development of a wearable sensoring system for human activity recognition focusing on hand and leg assessments. The research attempts to sufficiently recognize the motion to provide physiotherapist about the patient condition in the remote area. The system is designed by applying Arduino as the main controller with the help of accelerometer to identify human movements and then classifying them into soft, medium and hard motions categories. From the research, data acquired from the assessment is then imported into Microsoft Excel by using Guino software to describe the human motions. The accelerometer sensors are placed as follows; the on the right hand for three positions which are on the wrist, on the elbow, and on the shoulder. Meanwhile on right leg for three position which is in tight, calf and ankle. Experimental results show that the proposed system is capable to provide reliable information to both patient and physiotherapist about the motions. The recognition for the activity is based on physiotherapist consultation which provides early descriptions of human various activities using hands and legs. The proposed system can be applied for rehabilitation and monitoring system to realize a home-based smart monitoring and assessment system.

Keywords

Activity recognition Wearable system Accelerometer Hand and leg assessments 

References

  1. 1.
    Suto J, Oniga S (2017) Recognition rate difference between real-time and offline human activity recognition. In: International conference on internet of things for the global community, pp 1–5Google Scholar
  2. 2.
    Usman M et al (2018) On the correlation of sensor location and human activity recognition in body area networks (BANs). IEEE Syst J 12(1):82–91CrossRefGoogle Scholar
  3. 3.
    Wei Z, Bao T (2016)Research on a novel strategy for automatic activity recognition using wearable device. In: 8th IEEE international conference on communication software and networks (ICCSN), pp 488–492Google Scholar
  4. 4.
    Gaglio S, Re GL, Morana M (2014) Human activity recognition process using 3-D posture data. IEEE Trans Hum Mach Syst 45(5):586–597Google Scholar
  5. 5.
    Song B, Kamal AT, Soto C, Ding C, Farrell JA, Roy-Chowdhury AK (2010) Tracking and activity recognition through consensus in distributed camera networks. IEEE Trans Image Process 19(10):2564–2579MathSciNetCrossRefGoogle Scholar
  6. 6.
    Noor S, Uddin V (2018) Using context from inside-out vision for improved activity recognition. IET Comput Vision 12(3):276–287CrossRefGoogle Scholar
  7. 7.
    Fullerton E, Heller B, Munoz-Organero M (2017) Recognizing human activity in free-living using multiple body-worn accelerometers. IEEE Sens J 17(16):5290–5297CrossRefGoogle Scholar
  8. 8.
    Hsu YL, Yang SC, Chang HC, Lai HC (2018) Human daily and sport activity recognition using a wearable inertial sensor network. IEEE Access 6:31715–31728CrossRefGoogle Scholar
  9. 9.
    Lee SM, Yoon SM, Cho H (2017) Human activity recognition from accelerometer data using convolutional neural network. In: IEEE international conference on big data and smart computing (BigComp), Jeju, pp 131–134Google Scholar
  10. 10.
    Mohamed R, Perumal T, Sulaiman MN, Mustapha N, Razali MN (2017) Conflict resolution using enhanced label combination method for complex activity recognition in smart home environment. In: IEEE 6th global conference on consumer electronics (GCCE), Nagoya, pp 1–3Google Scholar
  11. 11.
    Perumal T, Chui YL, Ahmadon MAB, Yamaguchi S (2017) IoT based activity recognition among smart home residents. In: IEEE 6th global conference on consumer electronics (GCCE), Nagoya, pp 1–2Google Scholar
  12. 12.
    Azkune G, Almeida A (2018) A scalable hybrid activity recognition approach for intelligent environments. IEEE Access 6:41745–41759CrossRefGoogle Scholar
  13. 13.
    Chiang SY, Kan YC, Tu YC, Lin HC (2012) Activity recognition by fuzzy logic system in wireless sensor network for physical therapy. In: Watada J, Watanabe T, Phillips-Wren G, Howlett R, Jain L (eds) Intelligent decision technologies. Smart innovation, systems and technologies, vol 16. Springer, Berlin, Heidelberg, pp 191–200CrossRefGoogle Scholar
  14. 14.
    Zhang H, Zhou W, Parker LE (2015) Fuzzy temporal segmentation and probabilistic recognition of continuous human daily activities. IEEE Trans Hum Mach Syst 45(5):598–611CrossRefGoogle Scholar
  15. 15.
    Kolekar MH, Bharti N, Patil PN (2016) Detection of fence climbing using activity recognition by support vector machine classifier. In: IEEE region 10 conference, Singapore, pp 398–402Google Scholar
  16. 16.
    Nurhanim K, Elamvazuthi I, Izhar LI, Ganesan T (2017) Classification of human activity based on smartphone inertial sensor using support vector machine. In: IEEE 3rd international symposium in robotics and manufacturing automation, Kuala Lumpur, pp 1–5Google Scholar
  17. 17.
    Chen Y, Yu L, Ota K, Dong M (2018) Robust activity recognition for aging society. IEEE J Biomed Health Informatics 22(6):1754–1764CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Hamzah Ahmad
    • 1
    Email author
  • Nurul Syafiqah Mohd
    • 1
  • Nur Aqilah Othman
    • 1
  • Mohd Mawardi Saari
    • 1
  • Mohd Syakirin Ramli
    • 1
  1. 1.Faculty of Electrical and Electronics EngineeringUniversiti Malaysia PahangPekanMalaysia

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