Situation Awareness Inferred from Posture Transition and Location; derived from smart phone and smart home sensors

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Situation awareness may be inferred from user context such as body posture transition and location data. Smart phones and smart homes incorporate sensors that can record this information without significant inconvenience to the user. Algorithms were developed to classify activity postures to infer current situations; and to measure user’s physical location, in order to provide context that assists such interpretation. Location was detected using a subarea-mapping algorithm; activity classification was performed using a hierarchical algorithm with backward reasoning; and falls were detected using fused multiple contexts (current posture, posture transition, location and heart rate) based on two models: ‘certain fall’ and ‘possible fall’. The approaches were evaluated on nine volunteers using a smartphone, which provided accelerometer and orientation data, and an RFID network deployed at an indoor environment. Experimental results illustrated falls detection sensitivity of 94.7% and specificity of 85.7%. By providing appropriate context the robustness of situation recognition algorithms can be enhanced.
LanguageEnglish
Pages814-821
JournalIEEE Transactions on Human-Machine Systems
Volume47
Issue number6
Early online date28 Apr 2017
DOIs
Publication statusE-pub ahead of print - 28 Apr 2017

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Sensors
Smartphones
Accelerometers
Radio frequency identification (RFID)

Keywords

  • Assisted living
  • Body sensor networks
  • Context awareness
  • Wearable computers

Cite this

@article{c1efa81db7d142fa8093d11d1f7ad339,
title = "Situation Awareness Inferred from Posture Transition and Location; derived from smart phone and smart home sensors",
abstract = "Situation awareness may be inferred from user context such as body posture transition and location data. Smart phones and smart homes incorporate sensors that can record this information without significant inconvenience to the user. Algorithms were developed to classify activity postures to infer current situations; and to measure user’s physical location, in order to provide context that assists such interpretation. Location was detected using a subarea-mapping algorithm; activity classification was performed using a hierarchical algorithm with backward reasoning; and falls were detected using fused multiple contexts (current posture, posture transition, location and heart rate) based on two models: ‘certain fall’ and ‘possible fall’. The approaches were evaluated on nine volunteers using a smartphone, which provided accelerometer and orientation data, and an RFID network deployed at an indoor environment. Experimental results illustrated falls detection sensitivity of 94.7{\%} and specificity of 85.7{\%}. By providing appropriate context the robustness of situation recognition algorithms can be enhanced.",
keywords = "Assisted living, Body sensor networks, Context awareness, Wearable computers",
author = "Shumei Zhang and McCullagh, {P. J.} and HR Zheng and Chris Nugent",
note = "Reference text: [1] C. D. Nugent, M. D. Mulvenna, X. Hong, & S. Devlin, “Experiences in the development of a smart lab,” International Journal of Biomedical Engineering and Technology, 2(4):319-331, 01 2009. [2] J. Teno, D. P. Kiel, V. Mor, {"}Multiple stumbles: a risk factor for falls in community-dwelling elderly,{"} J. America Geriatrics Society, 38(12), pp. 1321-1325, 1990. [3] B. Najafi, K. Aminian, F. Loew, Y. Blanc and P. A. Robert, {"}Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly,{"} in IEEE Transactions on Biomedical Engineering, 49(8), 843-851, 2002. [4] M. B. King, & M. E. Tinetti, {"}Falls in community-dwelling older persons{"}, Journal of the American Geriatrics Society, 43(10), 1146-1154, 1995. [5] G. Demiris, & B. K. Hensel, “Technologies for an aging society: a systematic review of {"}smart home{"} applications,” Yearbook of Medical Informatics, 3, 33-40, 2008. [6] H. Hawley-Hague, E. Boulton, “Older adults’ perceptions of technologies aimed at falls prevention, detection or monitoring: a systematic review”, Inter. Journal of Medical Informatics, 83(6), 416–426, 2014. [7] A. Staranowicz, G. R. Brown, G.L. Mariottini, “Evaluating the accuracy of a mobile Kinect-based gait-monitoring system for fall prediction”, PETRA 13, 1-57:4, ACM New York, 2013. [8] U. Anliker, J. Ward, P. Lukowicz, {"}AMON: a wearable multiparameter medical monitoring and alert system{"}, IEEE Transactions on Information Technology in Biomedicine, vol. 8, no. 4, pp. 415-427, 2004. [9] Visonic Inc., {"}Fall detector MCT-241MD PERS{"}, online available: http://www.visonic.com/Products/Wireless-Emergency-Response-Systems/Fall-detector-mct-241md-pers-wer , visited in 2016. [10] M. Kangas, A. Konttila, I. Winblad, & T. Jamsa, {"}Determi- nation of simple thresholds for accelerometry-based param-eters for fall detection,{"} In Proceedings of the IEEE EMBS, pages 1367- 1370, 2007. [11] U. Lindemann, A. Hock, M. Stuber & W. Keck, et al., {"}Evaluation of a fall detector based on accelerometers: A pilot study{"}, Medical and Biological Engineering and Computing, 43 (5), pp. 548-551, 2005. [12] S. Abbate, M. Avvenuti, F. Bonatesta, & G. Cola, “A smartphone-based fall detection system”, Pervasive & Mobile Computing, 8(6), 883-899, 2012. [13] J. T. Zhang, A. C. Novak, B. Brouwer & Q. Li, “Concurrent validation of Xsens MVN measurement of lower limb joint angular kinematics”, Physiological measurement, 34(8), N63, 2013. [14] F. Bianchi, S. J. Redmond, et al. “Barometric Pressure and Triaxial Accelerometry-based Falls Event Detection{"}, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18 (6), 619-627, 2010. [15] Q. Li, J. A. Stankovic, M. A. Hanson, A. T. Barth, J. Lach & G. Zhou, {"}Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information,{"} Body Sensor Networks, 138-143, June 2009. [16] M. Lustrek, & B. Kaluza, “Fall detection and activity recognition with machine learning”, Informatica, 33(2), 197-204, 2009. [17] H. Gjoreski, M. Gams, & M. Luštrek, “Context-based fall detection and activity recognition using inertial and location sensors”, Journal of Ambient Intelligence & Smart Environments, 6(4), 419-433, 2014. [18] N. Y. Ko, T. Y. Kuc, “Fusing range measurements from ultrasonic beacons and a laser range finder for localization of a mobile robot.” Sensors 15(5), 11050-75, 2015. [19] T. Yamazaki, {"}Beyond the smart home{"}, Proceedings of the International Conference on Hybrid Information Technology, 350-355, 2006. [20] C. C .Hsu, & P. C. Yuan, {"}The design and implementation of an intelligent deployment system for RFID readers{"}, Expert Systems with Applications, 38 (8), pp. 10506-10517, 2011. [21] J. H. Seok, J. Y. Lee, C. Oh, et al, {"}RFID Sensor Deployment using Differential Evolution for Indoor Mobile Robot Localization{"}, The IEEE Inter. Conference on Intelligent Robots and Systems, 3719-3724, 2010. [22] A. T. Murray, K. Kim, J. W. Davis, R. Machiraju, & R.E Parent, {"}Coverage optimization to support security monitoring,{"} Computers, Environment and Urban Systems, 31(2), pp. 133-147, 2007. [23] F. Lin, & P. Chiu, {"}A simulated annealing algorithm for energy efficient sensor network design{"}, Inter. Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, pp. 183-189, 2005. [24] A. W. Reza,, & T. K. Geok, {"}Investigation of indoor location sensing via RFID reader network utilizing grid covering algorithm{"}, Journal of Wireless Personal Communications, 49(1), 67-80, 2009. [25] J. Hightower, R. Want, & G. Borriello, {"}SpotON: An indoor 3D location sensing technology based on RF signal strength{"}, UW CSE 00-02-02, University of Washington, 2000. [26] J. Zhou & J. Shi, {"}RFID localization algorithms and applications-a review{"}, Journal of Intelligent Manufacturing, vol. 20(6), 695-707, 2009. [27] K. Lorincz & M. Welsh, {"}MoteTrack: a robust, decentralized approach to RF-based location tracking{"}, Personal and Ubiquitous Computing, vol. 11, no. 6, pp. 489-503, 2007. [28] X. Nguyen, M. I. Jordan, & B. Sinopoli, {"}A kernel-based learning approach to ad hoc sensor network localization{"}, ACM Transactions on Sensor Networks (TOSN), vol. 1, no. 1, pp. 134-152, 2005. [29] L. M .Ni, Y. Liu, Y. C .Lau, & A.P. Patil, {"}LANDMARC: indoor location sensing using active RFID{"}, Wireless Networks, 10(6), 701-710, 2004. [30] S. Zhang, P. McCullagh, J. Zhang, & T. Yu, “A smartphone based real-time daily activity monitoring system”, Cluster Computing, 17(3), 711-721, 2014. [31] S Zhang, P McCullagh, C. Nugent, H. Zheng, N. Black, {"}A Subarea Mapping Approach for Indoor Localisation{"}, proceedings on toward useful services for elderly and people with disabilities, 80-87,2011. [32] S. Zhang, P McCullagh, {"}RFID Network Deployment Approaches for Indoor Localisation{"}, Proceedings of the 12th International conference on body sensor networks, Boston, USA, 2015. [33] C. C. Chang, C. J. Lin, “LIBSVM: a library for support vector machines”, 2(3):27, 2011. http://www.csie.ntuedu.tw/cjlin/libsvm, [34] S. Zhang, P. McCullagh, C. Nugent, H. Zheng, et al., {"}Optimal model selection for posture recognition in home-based healthcare{"}, Inter. Journal of Machine Learning and Cybernetics, 2(1), 1-14, 2011. [35] Y. Kim, Y. Chon, & H. Cha, “Smartphone-based collaborative and autonomous radio fingerprinting,” IEEE Transactions on Systems Man & Cybernetics Part C, 42(1), 112-122, 2012. [36] S. Zhang, P Mccullagh, C Nugent, H Zheng, “Activity Monitoring Using a Smart Phone's Accelerometer with Hierarchical Classification”, IEEE Conference on Intelli. Environments pp.158- 163, 2010. [37] S. Zhang, H. Li, P. McCullagh, C. Nugent, & H. Zheng, {"}A Real-time Falls Detection System for Elderly{"}, IEEE Conference in Computer Science & Electronic Engineering (CEEC), pp.51-56, 2013. [38] D. Zhang, X. Shen, & X. Qi, “Resting heart rate and all-cause and cardiovascular mortality in the general population: a meta-analysis”, Canadian Medical Association Journal, 188(3):E53, 2016. [39] S. Zhang, P. McCullagh, C. Nugent, H. Zheng, “A theoretic algorithm for fall and motionless detection”. International Conference on Pervasive Computing Technologies for Healthcare, pp.1-6, 2009. [40] P. J. Mork, & R. H. Westgaard, “Back posture and low back muscle activity in female computer workers: a field study”, Clinical Biomechanics, 24(2), 169-175, 2009. [41] V. T. van Hees, R. Golubic, U. Ekelund, S. Brage, “Impact of study design on development and evaluation of an activity-type classifier”, Journal of Applied Physiology, 114(8), 1042–1051, 2013.",
year = "2017",
month = "4",
day = "28",
doi = "10.1109/THMS.2017.2693238",
language = "English",
volume = "47",
pages = "814--821",
journal = "IEEE Transactions on Human-Machine Systems",
issn = "2168-2291",
number = "6",

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TY - JOUR

T1 - Situation Awareness Inferred from Posture Transition and Location; derived from smart phone and smart home sensors

AU - Zhang, Shumei

AU - McCullagh, P. J.

AU - Zheng, HR

AU - Nugent, Chris

N1 - Reference text: [1] C. D. Nugent, M. D. Mulvenna, X. Hong, & S. Devlin, “Experiences in the development of a smart lab,” International Journal of Biomedical Engineering and Technology, 2(4):319-331, 01 2009. [2] J. Teno, D. P. Kiel, V. Mor, "Multiple stumbles: a risk factor for falls in community-dwelling elderly," J. America Geriatrics Society, 38(12), pp. 1321-1325, 1990. [3] B. Najafi, K. Aminian, F. Loew, Y. Blanc and P. A. Robert, "Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly," in IEEE Transactions on Biomedical Engineering, 49(8), 843-851, 2002. [4] M. B. King, & M. E. Tinetti, "Falls in community-dwelling older persons", Journal of the American Geriatrics Society, 43(10), 1146-1154, 1995. [5] G. Demiris, & B. K. Hensel, “Technologies for an aging society: a systematic review of "smart home" applications,” Yearbook of Medical Informatics, 3, 33-40, 2008. [6] H. Hawley-Hague, E. Boulton, “Older adults’ perceptions of technologies aimed at falls prevention, detection or monitoring: a systematic review”, Inter. Journal of Medical Informatics, 83(6), 416–426, 2014. [7] A. Staranowicz, G. R. Brown, G.L. Mariottini, “Evaluating the accuracy of a mobile Kinect-based gait-monitoring system for fall prediction”, PETRA 13, 1-57:4, ACM New York, 2013. [8] U. Anliker, J. Ward, P. Lukowicz, "AMON: a wearable multiparameter medical monitoring and alert system", IEEE Transactions on Information Technology in Biomedicine, vol. 8, no. 4, pp. 415-427, 2004. [9] Visonic Inc., "Fall detector MCT-241MD PERS", online available: http://www.visonic.com/Products/Wireless-Emergency-Response-Systems/Fall-detector-mct-241md-pers-wer , visited in 2016. [10] M. Kangas, A. Konttila, I. Winblad, & T. Jamsa, "Determi- nation of simple thresholds for accelerometry-based param-eters for fall detection," In Proceedings of the IEEE EMBS, pages 1367- 1370, 2007. [11] U. Lindemann, A. Hock, M. Stuber & W. Keck, et al., "Evaluation of a fall detector based on accelerometers: A pilot study", Medical and Biological Engineering and Computing, 43 (5), pp. 548-551, 2005. [12] S. Abbate, M. Avvenuti, F. Bonatesta, & G. Cola, “A smartphone-based fall detection system”, Pervasive & Mobile Computing, 8(6), 883-899, 2012. [13] J. T. Zhang, A. C. Novak, B. Brouwer & Q. Li, “Concurrent validation of Xsens MVN measurement of lower limb joint angular kinematics”, Physiological measurement, 34(8), N63, 2013. [14] F. Bianchi, S. J. Redmond, et al. “Barometric Pressure and Triaxial Accelerometry-based Falls Event Detection", IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18 (6), 619-627, 2010. [15] Q. Li, J. A. Stankovic, M. A. Hanson, A. T. Barth, J. Lach & G. Zhou, "Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information," Body Sensor Networks, 138-143, June 2009. [16] M. Lustrek, & B. Kaluza, “Fall detection and activity recognition with machine learning”, Informatica, 33(2), 197-204, 2009. [17] H. Gjoreski, M. Gams, & M. Luštrek, “Context-based fall detection and activity recognition using inertial and location sensors”, Journal of Ambient Intelligence & Smart Environments, 6(4), 419-433, 2014. [18] N. Y. Ko, T. Y. Kuc, “Fusing range measurements from ultrasonic beacons and a laser range finder for localization of a mobile robot.” Sensors 15(5), 11050-75, 2015. [19] T. Yamazaki, "Beyond the smart home", Proceedings of the International Conference on Hybrid Information Technology, 350-355, 2006. [20] C. C .Hsu, & P. C. Yuan, "The design and implementation of an intelligent deployment system for RFID readers", Expert Systems with Applications, 38 (8), pp. 10506-10517, 2011. [21] J. H. Seok, J. Y. Lee, C. Oh, et al, "RFID Sensor Deployment using Differential Evolution for Indoor Mobile Robot Localization", The IEEE Inter. Conference on Intelligent Robots and Systems, 3719-3724, 2010. [22] A. T. Murray, K. Kim, J. W. Davis, R. Machiraju, & R.E Parent, "Coverage optimization to support security monitoring," Computers, Environment and Urban Systems, 31(2), pp. 133-147, 2007. [23] F. Lin, & P. Chiu, "A simulated annealing algorithm for energy efficient sensor network design", Inter. Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, pp. 183-189, 2005. [24] A. W. Reza,, & T. K. Geok, "Investigation of indoor location sensing via RFID reader network utilizing grid covering algorithm", Journal of Wireless Personal Communications, 49(1), 67-80, 2009. [25] J. Hightower, R. Want, & G. Borriello, "SpotON: An indoor 3D location sensing technology based on RF signal strength", UW CSE 00-02-02, University of Washington, 2000. [26] J. Zhou & J. Shi, "RFID localization algorithms and applications-a review", Journal of Intelligent Manufacturing, vol. 20(6), 695-707, 2009. [27] K. Lorincz & M. Welsh, "MoteTrack: a robust, decentralized approach to RF-based location tracking", Personal and Ubiquitous Computing, vol. 11, no. 6, pp. 489-503, 2007. [28] X. Nguyen, M. I. Jordan, & B. Sinopoli, "A kernel-based learning approach to ad hoc sensor network localization", ACM Transactions on Sensor Networks (TOSN), vol. 1, no. 1, pp. 134-152, 2005. [29] L. M .Ni, Y. Liu, Y. C .Lau, & A.P. Patil, "LANDMARC: indoor location sensing using active RFID", Wireless Networks, 10(6), 701-710, 2004. [30] S. Zhang, P. McCullagh, J. Zhang, & T. Yu, “A smartphone based real-time daily activity monitoring system”, Cluster Computing, 17(3), 711-721, 2014. [31] S Zhang, P McCullagh, C. Nugent, H. Zheng, N. Black, "A Subarea Mapping Approach for Indoor Localisation", proceedings on toward useful services for elderly and people with disabilities, 80-87,2011. [32] S. Zhang, P McCullagh, "RFID Network Deployment Approaches for Indoor Localisation", Proceedings of the 12th International conference on body sensor networks, Boston, USA, 2015. [33] C. C. Chang, C. J. Lin, “LIBSVM: a library for support vector machines”, 2(3):27, 2011. http://www.csie.ntuedu.tw/cjlin/libsvm, [34] S. Zhang, P. McCullagh, C. Nugent, H. Zheng, et al., "Optimal model selection for posture recognition in home-based healthcare", Inter. Journal of Machine Learning and Cybernetics, 2(1), 1-14, 2011. [35] Y. Kim, Y. Chon, & H. Cha, “Smartphone-based collaborative and autonomous radio fingerprinting,” IEEE Transactions on Systems Man & Cybernetics Part C, 42(1), 112-122, 2012. [36] S. Zhang, P Mccullagh, C Nugent, H Zheng, “Activity Monitoring Using a Smart Phone's Accelerometer with Hierarchical Classification”, IEEE Conference on Intelli. Environments pp.158- 163, 2010. [37] S. Zhang, H. Li, P. McCullagh, C. Nugent, & H. Zheng, "A Real-time Falls Detection System for Elderly", IEEE Conference in Computer Science & Electronic Engineering (CEEC), pp.51-56, 2013. [38] D. Zhang, X. Shen, & X. Qi, “Resting heart rate and all-cause and cardiovascular mortality in the general population: a meta-analysis”, Canadian Medical Association Journal, 188(3):E53, 2016. [39] S. Zhang, P. McCullagh, C. Nugent, H. Zheng, “A theoretic algorithm for fall and motionless detection”. International Conference on Pervasive Computing Technologies for Healthcare, pp.1-6, 2009. [40] P. J. Mork, & R. H. Westgaard, “Back posture and low back muscle activity in female computer workers: a field study”, Clinical Biomechanics, 24(2), 169-175, 2009. [41] V. T. van Hees, R. Golubic, U. Ekelund, S. Brage, “Impact of study design on development and evaluation of an activity-type classifier”, Journal of Applied Physiology, 114(8), 1042–1051, 2013.

PY - 2017/4/28

Y1 - 2017/4/28

N2 - Situation awareness may be inferred from user context such as body posture transition and location data. Smart phones and smart homes incorporate sensors that can record this information without significant inconvenience to the user. Algorithms were developed to classify activity postures to infer current situations; and to measure user’s physical location, in order to provide context that assists such interpretation. Location was detected using a subarea-mapping algorithm; activity classification was performed using a hierarchical algorithm with backward reasoning; and falls were detected using fused multiple contexts (current posture, posture transition, location and heart rate) based on two models: ‘certain fall’ and ‘possible fall’. The approaches were evaluated on nine volunteers using a smartphone, which provided accelerometer and orientation data, and an RFID network deployed at an indoor environment. Experimental results illustrated falls detection sensitivity of 94.7% and specificity of 85.7%. By providing appropriate context the robustness of situation recognition algorithms can be enhanced.

AB - Situation awareness may be inferred from user context such as body posture transition and location data. Smart phones and smart homes incorporate sensors that can record this information without significant inconvenience to the user. Algorithms were developed to classify activity postures to infer current situations; and to measure user’s physical location, in order to provide context that assists such interpretation. Location was detected using a subarea-mapping algorithm; activity classification was performed using a hierarchical algorithm with backward reasoning; and falls were detected using fused multiple contexts (current posture, posture transition, location and heart rate) based on two models: ‘certain fall’ and ‘possible fall’. The approaches were evaluated on nine volunteers using a smartphone, which provided accelerometer and orientation data, and an RFID network deployed at an indoor environment. Experimental results illustrated falls detection sensitivity of 94.7% and specificity of 85.7%. By providing appropriate context the robustness of situation recognition algorithms can be enhanced.

KW - Assisted living

KW - Body sensor networks

KW - Context awareness

KW - Wearable computers

U2 - 10.1109/THMS.2017.2693238

DO - 10.1109/THMS.2017.2693238

M3 - Article

VL - 47

SP - 814

EP - 821

JO - IEEE Transactions on Human-Machine Systems

T2 - IEEE Transactions on Human-Machine Systems

JF - IEEE Transactions on Human-Machine Systems

SN - 2168-2291

IS - 6

ER -