Abstract
Movement detection is gaining more and more attention among various pattern recognition problems. Recognizing human movement activity types is extremely useful for fall detection for elderly people. Wireless sensor network technology enables human motion data from wearable wireless sensor devices be transmitted for remote processing. This paper studies methods to process the human motion data received from wearable wireless sensor devices for detecting different types of human movement activities such as sitting, standing, lying, fall, running, and walking. Machine learning methods K Nearest Neighbor algorithm (KNN) and the Back Propagation Neural Network (BPNN) algorithm are used to classify the activities from the data acquired from sensors based on sample data. As there are a large amount of real-time raw data received from sensors and there are noises associated with these data, feature construction and reduction are used to preprocess these raw sensor data obtained from accelerometers embedded in wireless sensing motes for learning and processing. The singular value decomposition (SVD) technique is used for constructing the enriched features. The enriched features are then integrated with machine learning algorithms for movement detection. The testing data are collected from five adults. Experimental results show that our methods can achieve promising performance on human movement recognition and fall detection.
Similar content being viewed by others
References
Karantonis DM, Narayanan MR, Mathie MJ, Lovell NH, Celler BG (2006) Implementation of a real-time human movement classifier using a tri-axial accelerometer for ambulatory monitoring. IEEE Trans Inf Technol Biomed 10(1):156
Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. In: Proc second international conference on pervasive computing, pp 1–17
Ding C (2009) A real time motion monitoring system. Master thesis, St. Francis Xavier University, Antigonish, Canada
Scanaill NC, Carew S, Barralon P, Noury N, Lyon D, Lyons GM (2006) A review of approaches to mobility telemonitoring of the elderly in their living environment. Ann Biomed Eng 34(4):547–563
Huang WY, Zhang J, Liu ZJ (2007) Activity recognition based on hidden Markov models. In: Knowledge science, engineering and management. LNCS, vol 4798, pp 532–537
Song KT, Wang YQ (2005) Remote activity monitoring of the elderly using a two-axis accelerometer. In: Proc CACS automatic control conference (EMBEC 2005)
Choi ES, Bamg WC, Kim DY, Kim SR Beatbox music phone: Gesture based interactive mobile phone using a tri-axis accelerometer Proc IEEE international conference industry technology, pp 97–102
Wang JYYangJS, Chen YP (2008) Using acceleration measurements for activity recognition: an effective learning algorithm for constructing neural classifiers. Pattern Recognit Lett 29:2213–2220
Ward JA, Lukowicz P, Troster G, Starner TE (2006) Activity recognition of assembly task using body-worn microphones and accelerometers. IEEE Trans Pattern Anal Mach Intell 28(10):1553–1567
Ravi N, Dandekar N, Mysore P, Littman ML (2005) Activity recognition from accelerometer data. In: Proc seventeenth innovative applications of artificial intelligence conference, pp 1541–1546
Yang JY, Chen YP, Lee GY, Liou SN, Wang JS (2007) Activity recognition using one triaxial accelerometer: a neuro-fuzzy classifier with feature reduction. In: ICEC 2007. LNCS, vol 4740, pp 395–400
Mathie MJ (2003) Monitoring and interpreting human movement patters using a tri-axial accelerometer. Ph.D thesis, Univ. New South Wales, Sydney, Australia
Chen YP, Yang JY, Liou SN, Lee GY, Wang JS (2008) Online classifier construction algorithm for human activity detection using a tri-axial accelerometer. Appl Math Comput 205:849–860
Wang S, Yang J, Chen N, Chen X, Zhang Q (2005) Human activity recognition with user-free accelerometers in the sensor networks. In: Proc IEEE int conf neural networks and brain, vol 2, pp 1212–1217
Noorinaeini A, Lehto MR (2006) Hybrid singular value decomposition; a model of human text classification. Int J Hum Factors Model Simul 1(1):95–118
Yany Y (1995) Noise reduction in a statistical approach to text categorization. In: Proc 18th ACM international conference on research and development in information retrieval, New York, pp 256–263
Deerwester SC, Dumais ST, Landauer TK, Furnas GW, Harshman RA (1990) Indexing by latent semantic analysis. J Am Soc Inf Sci 41(6):391–407
Li CH, Park SC (2009) An efficient document classification model using an improved back propagation neural network and singular value decomposition. Expert Syst Appl 36:3208–3215
Park SB, Lee JW, Kim SK (2004) Content-based image classification using a neural network. Pattern Recognit Lett 25(3):287–300
Chapelle O, Haffner P, Vapnik VN (1999) Support vector machines for histogram-based image classification. IEEE Trans Neural Netw 10(5):1055–1064
Neuman L, Kozlowski J, Zgrzywa A (2004) Information retrieval using Bayesian networks. In: Proc ICCS 2004. LNCS, vol 3038, pp 521–528
Lawrence S, Giles CL, Fong S (2000) Natural language grammatical inference with recurrent neural networks. IEEE Trans Knowl Data Eng 12(1):126–140
Veropoulos K (2001) Machine learning approaches to medical decision making. PhD thesis, University of Bristol, March 2001
Graves A, Schmidhuber J (2008) Offline handwriting recognition with multidimensional recurrent neural networks. In: Proc NIPS 2008
Health report, Canada (2009) http://www.health.gov.on.ca/english/providers/program/ohtac/pdf/progress/2009/falls_en.pdf
Mica2 Datasheet, http://bullseye.xbow.com:81/Products/Product_pdf_files/Wireless_pdf/MICA2_Datasheet.pdf
Dai J, Bai X, Yang Z, Shen Z, Xuan D (2010) PerFallD: a pervasive fall detection system using mobile phones. In: Personal and ubiquitous computing, April
Zheng NG, Wu ZH, Lin M, Yang LT (2010) Enhancing battery efficiency for pervasive health monitoring systems based on electronic-textiles. IEEE Trans Inf Technol Biomed 14(2):350–359
Yang L, Lin M, Yang LT (2009) Integrating preemption threshold to fixed priority DVS scheduling algorithms. In: Proceedings of the 15th IEEE international conference on embedded and real-time computing systems and applications (RTCSA’09), Beijing, China, 24–26 August, 2009, pp 165–171
Li CH, Yang LT, Lin M (2013) Parallel training of an improved neural network for text categorization. Int J Parallel Program. doi:10.1007/s10766-013-0245-x
Lai CF, Lin M, Wen YG, Ma YW, Chen JL (2012) Applied lightweight parallel multi-appliance recognition on smart meter. In: The 15th IEEE international conference on computational science and engineering (CSE 2012), Paphas, Cyprus, 5–7 December, 2012, pp 361–366
Lin M (2000) Timing analysis of PL programs. J Control Eng Practice 8:697–703
Lin M, Malec J (1998) Timing analysis of reactive rule-based programs. J Control Eng Practice 6:403–408
Xie T, Qin X, Sung AH, Lin M, Yang LT (2006) Real-time scheduling with quality of security constraints. Int High Perform Comput Networking 4(3):188–197
Lin M, Xu L, Yang LT, Qin X, Zheng NG, Wu ZH, Qiu MK (2009) Static security optimization for real time systems. IEEE Trans Ind Inform 5(1):22–37
Yang LT, Xu L, Lin M (2005) Integer factorization by a parallel GNFS algorithm for public key cryptosystems. In: Embedded software and systems. LNCS, vol 3820, pp 683–695
Lin M, Ding C (2007) Parallel genetic algorithms for DVS scheduling of distributed embedded systems. In: High performance computing and communications. LNCS, vol 4782, pp 180–191
Pan YW, Lin M, Yang LT (2011) Reducing total energy for reliability-aware DVS algorithms. In: Ubiquitous intelligence and computing—8th international conference (UIC 2011), Banff, Canada, 2–4 September, 2011, pp 576–589
Yang L, Lin M, Yang LT (2012) Multi-core fixed priority DVS scheduling. In: The 12th international conference on algorithms and architectures for parallel processing (ICA3PP 2012), Fukuka, Japan, 4–7 September, 2012, pp 517–530
Zhao G, Huang B, Liu G, Guo Y, Mei Z, Wang L (2011) A low-cost body sensor networks for elderly activities recognition and fall prediction. In: Proceedings of the international bionic engineering conference 2011, Boston, USA, 18–20 September, 2011
Bagalà F, Becker C, Cappello A, Chiari L, Aminian K et al (2012) Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS ONE 7(5):e37062. doi:10.1371/journal.pone.0037062
Benini L, De Micheli G (1999) Energy-efficient design of battery-powered embedded systems. In: Low power electronics and design: Proceedings of 1999 international symposium on date of conference, Aug 1999, pp 212–217
Lin M (1999) Synthesis of control software in a layered architecture from hybrid automata. In: Hybrid systems: computation and control. LNCS, vol 1569. Springer, Berlin, pp 152–164
Lin M, Pan YW, Yang LT, Guo MY, Zheng NG (2013) Scheduling co-design for reliability and energy in cyber-physical systems. IEEE Trans Emerg Top Comput. doi:10.1109/TETC.2013.2274042
Qiu M, Sha E, Liu M, Lin M, Hua S, Yang LT (2008) Energy minimization with loop fusion and multi-functional-unit scheduling for multidimensional DSP. J Parallel Distrib Comput 68(4):443–455
Zhang Z, Kapoor U, Narayanan M, Lovell NH, Redmond SJ (2011) Design of an unobtrusive wireless sensor network for nighttime falls detection. IEEE Eng Med Biol Soc
Acknowledgement
This work was supported by NSERC (Natural Sciences and Engineering Research Council, Canada).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Li, C., Lin, M., Yang, L.T. et al. Integrating the enriched feature with machine learning algorithms for human movement and fall detection. J Supercomput 67, 854–865 (2014). https://doi.org/10.1007/s11227-013-1056-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-013-1056-y