Acquisition of patient kinematics in different environments plays an important role in the detection of risk situations such as fall detection in elderly patients, in rehabilitation of patients with injuries, and in the design of treatment plans for patients with neurological diseases. Received Signal Strength Indicator (RSSI) measurements in a Body Area Network (BAN), capture the signal power on a radio link. The main aim of this paper is to demonstrate the potential of utilizing RSSI measurements in assessment of human kinematic features, and to give methods to determine these features. RSSI measurements can be used for tracking different body parts’ displacements on scales of a few centimeters, for classifying motion and gait patterns instead of inertial sensors, and to serve as an additional reference to other sensors, in particular inertial sensors. Criteria and analytical methods for body part tracking, kinematic motion feature extraction, and a Kalman filter model for aggregation of RSSI and inertial sensor were derived. The methods were verified by a set of experiments performed in an indoor environment. In the future, the use of RSSI measurements can help in continuous assessment of various kinematic features of patients during their daily life activities and enhance medical diagnosis accuracy with lower costs.
References
[1]
Chaczko, Z.; Kale, A.; Chiu, C. Intelligent Health Care 2014; A Motion Analysis System for Health Practitioners. Proceedings of the 2010 Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Brisbane, Australia, 7–10 December 2010; pp. 303–308.
[2]
Beynon, S.; McGinley, J.L.; Dobson, F.; Baker, R. Correlations of the gait profile score and the movement analysis profile relative to clinical judgments. Gait Posture 2010, 32, 129–132.
[3]
Sekine, M.; Tamura, T.; Akay, M.; Fujimoto, T.; Togawa, T.; Fukui, Y. Discrimination of walking patterns using wavelet-based fractal analysis. IEEE Trans. Neural Syst. Rehabil. Eng. 2002, 10, 188–196.
[4]
Campo, E.; Bonhomme, S.; Chan, M.; Esteve, D. Remote Tracking Patients in Retirement Home Using Wireless Multisensor System. Proceedings of the 2010 12th IEEE International Conference on e-Health Networking Applications and Services (Healthcom), Lyon, France, 1–3 July 2010; pp. 226–230.
[5]
Alan, C.B. Handbook of Image and Video Processing 2005; Academic Press, Inc.: Cambridge, MA, USA, 2005.
[6]
Kanaujia, A.; Haering, N.; Taylor, G.; Bregler, C. 3D Human Pose and Shape Estimation from Multi-View Imagery. Proceedings of the 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Colorado Springs, CO, USA, 21–23 June 2011; pp. 49–56.
[7]
Ram, S.S.; Li, Y.; Lin, A.; Ling, H. Doppler-based detection and tracking of humans in indoor environments. J. Frankl. Inst. 2008, 345, 679–699.
[8]
Blumrosen, G.; Uziel, M.; Rubinsky, B.; Porrat, D. Noncontact tremor characterization using low-power wideband radar technology. IEEE Trans. Biomed. Eng. 2012, 59, 674–686.
[9]
Jaeyong, S.; Ponce, C.; Selman, B.; Saxena, A. Unstructured Human Activity Detection from RGBD Images. Proceedings of the 2012 IEEE International Conference on Robotics and Automation (ICRA), St. Paul, MN, USA, 14–18 May 2012; pp. 842–849.
[10]
Shotton, J.; Sharp, T.; Kipman, A.; Fitzgibbon, A.; Finocchio, M.; Blake, A.; Cook, M.; Moore, R. Real-time human pose recognition in parts from single depth images. Commun. ACM 2013, 56, 116–124.
[11]
Bachmann, C.; Ashouei, M.; Pop, V.; Vidojkovic, M.; Groot, H.D.; Gyselinckx, B. Low-power wireless sensor nodes for ubiquitous long-term biomedical signal monitoring. IEEE Commun. Mag. 2012, 50, 20–27.
[12]
Hanson, M.A.; Powell, H.C.; Barth, A.T.; Ringgenberg, K.; Calhoun, B.H.; Aylor, J.H.; Lach, J. Body area sensor networks: Challenges and opportunities. Computer 2009, 42, 58–65.
[13]
Gu, Y.; Lo, A.; Niemegeers, I. A survey of indoor positioning systems for wireless personal networks. IEEE Commun. Surv. Tutor. 2009, 11, 13–32.
[14]
Titterton, D.; Weston, J.; Titterton, D.; Weston, J. Strapdown Inertial Navigation Technology; Institution of Electrical Engineers: London, UK, 2004.
[15]
Barbour, N.; Schmidt, G. Inertial sensor technology trends. IEEE Sens. J. 2001, 1, 332–339.
[16]
Luinge, H.; Veltink, P. Measuring orientation of human body segments using miniature gyroscopes and accelerometers. Med. Biol. Eng. Comput. 2005, 43, 273–282.
[17]
Sabatini, A.M.; Martelloni, C.; Scapellato, S.; Cavallo, F. Assessment of walking features from foot inertial sensing. IEEE Trans. Biomed. Eng. 2005, 52, 486–494.
[18]
Alvarez, J.C.; Gonzalez, R.C.; Alvarez, D.; Lopez, A.M.; Rodriguez-Uria, J. Multisensor Approach to Walking Distance Estimation with Foot Inertial Sensing. Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2007), Lyon, France, 23–26 August 2007; pp. 5719–5722.
[19]
Bennett, T.; Jafari, R.; Gans, N. An Extended Kalman Filter to Estimate Human Gait Parameters and Walking Distance. Proceedings of the The American Control Conference (ACC), Washington, DC, USA, 17–19 June 2013.
[20]
Rantakokko, J.; Rydell, J.; Stromback, P.; Handel, P.; Callmer, J.; Tornqvist, D.; Gustafsson, F.; Jobs, M.; Gruden, M. Accurate and reliable soldier and first responder indoor positioning: Multisensor systems and cooperative localization. IEEE Wirel. Commun. 2011, 18, 10–18.
[21]
Anzai, D.; Hara, S. An RSSI-Based MAP Localization Method with Channel Parameters Estimation in Wireless Sensor Networks. Proceedings of IEEE 69th Vehicular Technology Conference (VTC Spring 2009), Barcelona, Spain, 26–29 April 2009.
[22]
Chung, W.Y. Enhanced RSSI-Based Real-Time User Location Tracking System for Indoor and Outdoor Environments. Proceedings of the 2007 International Conference on Convergence Information Technology, Gyeongju, Korea, 21–23 November, 2007; pp. 1213–1218.
[23]
Wang, J.; Prasad, R.V.; An, X.; Niemegeers, I.G.M.M. A study on wireless sensor network based indoor positioning systems for context-aware applications. Wirel. Commun. Mob. Comput. 2012, 12, 53–70.
[24]
Zanca, G.; Zorzi, F.; Zanella, A.; Zorzi, M. Experimental Comparison of RSSI-based Localization Algorithms for Indoor Wireless Sensor Networks. Proceedings of the Workshop on Real-World Wireless Sensor Networks (REALWSN'08), Glasgow, UK, 1 April 2008; pp. 1–5.
[25]
Stoyanova, T.; Kerasiotis, F.; Prayati, A.; Papadopoulos, G. Evaluation of Impact Factors on RSS Accuracy for Localization and Tracking Applications. Proceedings of the 5th ACM International Workshop on Mobility Management and Wireless Access, Chicago, IL, USA, 24 September 2007; p. 16.
[26]
Guraliuc, A.R.; Serra, A.A.; Nepa, P.; Manara, G.; Potorti, F. Detection and Classification of Human Arm Movements for Physical Rehabilitation. Proceedings of the 2010 IEEE Antennas and Propagation Society International Symposium (APSURSI), Toronto, ON, Canada, 11–17 July 2010.
[27]
Quwaider, M.; Biswas, S. Body Posture Identification Using Hidden Markov Model with a Wearable Sensor Network. Proceedings of the ICST 3rd International Conference on Body Area Networks, Tempe, AZ, USA, 30 September–2 October 2008; pp. 1–8.
[28]
Saxena, M.; Gupta, P.; Jain, B.N. Experimental Analysis of RSSI-Based Location Estimation in Wireless Sensor Networks. Proceedings of the 3rd International Conference on Communication Systems Software and Middleware and Workshops (COMSWARE 2008), Bangalore, India, 5–10 January 2008; pp. 503–510.
[29]
Blumrosen, G.; Hod, B.; Anker, T.; Dolev, D.; Rubinsky, B. Enhanced calibration technique for RSSI-based ranging in body area networks. Ad Hoc Netw. 2013, 11, 555–569.
Miluzzo, E.; Zheng, X.; Fodor, K.; Campbell, A.T. Radio Characterization of 802.15. 4 and Its Impact on the Design of Mobile Sensor Networks. Proceedings of the 5th European Conference on Wireless Sensor Networks, Bologna, Italy, 30 January–1 February 2008; pp. 171–188.
[32]
Mao, G.; Anderson, B.; Fidan, B. Path loss exponent estimation for wireless sensor network localization. Comput. Netw. 2007, 51, 2467–2483.
[33]
Papamanthou, C.; Preparata, F.P.; Tamassia, R. Algorithms for Location Estimation Based on RSSI Sampling. Lect. Notes Comput. Sci. 2008, 5389, 86.
[34]
Awad, A.; Frunzke, T.; Dressler, F. Adaptive Distance Estimation and Localization in WSN Using RSSI Measures. Proceedings of the 10th Euromicro Conference on Digital System Design Architectures, Methods and Tools (DSD 2007), Lubeck, Germany, 29–31 August 2007; pp. 471–478.
[35]
Blumrosen, G.; Hod, B.; Anker, T.; Dolev, D.; Rubinsky, B. Continuous Close-Proximity RSSI-Based Tracking in Wireless Sensor Networks. Proceedings of the 2010 International Conference on Body Sensor Networks, Singapore, 7–9 June 2010; pp. 234–239.
[36]
Patwari, N.; Hero, A.O., III. Using Proximity and Quantized RSS for Sensor Localization in Wireless Networks. Proceedings of the 2nd ACM International Conference on Wireless Sensor Networks and Applications, San Diego, CA, USA, 19 September 2003; pp. 20–29.
[37]
Er-Jie, Z.; Ting-Zhu, H. Geometric Dilution of Precision in Navigation Computation. Proceedings of the 2006 International Conference on Machine Learning and Cybernetics, Dalian, China, 13–16 August 2006; pp. 4116–4119.
[38]
Liao, W.H.; Lee, Y.C. A Lightweight Localization Scheme in Wireless Sensor Networks. Proceedings of the International Conference on Wireless and Mobile Communications (ICWMC'062006), Bucharest, Romanian, 29–31 July 2006.
[39]
Tukey, J.W. Exploratory Data Analysis; Addison-Wesley: Reading, MA, USA, 1977.
[40]
Bloem, B.R.; Hausdorff, J.M.; Visser, J.E.; Giladi, N. Falls and freezing of gait in Parkinson's disease: A review of two interconnected, episodic phenomena. Mov. Disord. 2004, 19, 871–884.
[41]
Atkinson, P.M.; Tatnall, A.R.L. Introduction Neural networks in remote sensing. Int. J. Remote Sens. 1997, 18, 699–709.
[42]
Yang, J.; Lu, H.; Liu, Z.; Boda, P.P. Physical Activity Recognition with Mobile Phones: Challenges, Methods, and Applications Multimedia Interaction and Intelligent User Interfaces; Shao, L., Shan, C., Luo, J., Etoh, M., Eds.; Springer: London, UK, 2010; pp. 185–213.
[43]
Brach, J.S.; Studenski, S.A.; Perera, S.; VanSwearingen, J.M.; Newman, A.B. Gait variability and the risk of incident mobility disability in community-dwelling older adults. J. Gerontol. Ser. A 2007, 62, 983–988.
[44]
Curt, A.; Schwab, M.E.; Dietz, V. Providing the clinical basis for new interventional therapies: Refined diagnosis and assessment of recovery after spinal cord injury. Spinal Cord 2004, 42, 1–6.
[45]
Hache, G.; Lemaire, E.D.; Baddour, N. Mobility Change-of-State Detection Using a Smartphone-Based Approach. Proceedings of the 2010 IEEE International Workshop on Medical Measurements and Applications Proceedings (MeMeA), Ottawa, ON, Canada, 30 April–1 May 2010; pp. 43–46.
[46]
Caderby, T.; Yiou, E.; Peyrot, N.; Bonazzi, B.; Dalleau, G. Detection of swing heel-off event in gait initiation using force-plate data. Gait Posture 2012, doi:10.1016/j.gaitpost.2012.08.011.
[47]
Kung-Chung, L.; Oka, A.; Pollakis, E.; Lampe, L. A Comparison between Unscented Kalman Filtering and Particle Filtering for RSSI-based Tracking. Proceedings of the 2010 7th Workshop on Positioning Navigation and Communication (WPNC), Dresden, Germany, 11–12 March 2010; pp. 157–163.
[48]
Brown, R.G.; Hwang, P.Y.C. Introduction to Random Signals and Applied Kalman Filtering: With {MATLAB} Exercises and Solutions; John Wiley & Sons, Inc.: New York, NY, USA, 1997.
[49]
Won, S.H.P.; Melek, W.W.; Golnaraghi, F. A kalman/particle filter-based position and orientation estimation method using a position sensor/inertial measurement unit hybrid system. IEEE Trans. Ind. Electron. 2010, 57, 1787–1798.
[50]
Roetenberg, D.; Slycke, P.J.; Veltink, P.H. Ambulatory position and orientation tracking fusing magnetic and inertial sensing. IEEE Trans. Biomed. Eng. 2007, 54, 883–890.
[51]
Brookner, E. Tracking and Kalman Filtering Made Easy1998; Wiley-Interscience: New York, NY, USA, 1998.
[52]
Luttwak, A. Human Motion Tracking and Orientation Estimation Using Inertial Sensors and RSSI Measurements. M.Sc. Thesis, School of Computer Science, Hebrew University, Jerusalem, Israel, 2001.
[53]
Bortz, J.E. A New Mathematical Formulation for Strapdown Inertial Navigation. IEEE Trans. Aerosp. Electr. Syst. 1971, AES-7(1), 61–66.
[54]
Xiaofang, W.; Yan, X.; Haibin, S.; Jianghong, S. A Robust Ranging-Based Localization Mechanism in Wireless Sensor Networks. Proceedings of the 3rd International Conference on Anti-Counterfeiting, Security, and Identification in Communication (ASID 2009), Piscataway, NJ, USA, 20–22 August 2009; pp. 413–416.
