A review of wearable sensors and systems with application in rehabilitation

Shyamal Patel, Hyung Park, Paolo Bonato, Leighton Chan, Mary Rodgers, Shyamal Patel, Hyung Park, Paolo Bonato, Leighton Chan, Mary Rodgers

Abstract

The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques) that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed.

Figures

Figure 1
Figure 1
Illustration of a remote health monitoring system based on wearable sensors. Health related information is gathered via body-worn wireless sensors and transmitted to the caregiver via an information gateway such as a mobile phone. Caregivers can use this information to implement interventions as needed.
Figure 2
Figure 2
Flexible wireless ECG sensor with a fully functional microcontroller by IMEC. Developments in the field of flexible electronics are expected to lead to the advent of smaller, lighter and more comfortable wearable systems. (Courtesy of IMEC, The Netherlands).
Figure 3
Figure 3
Example of e-textile system for remote, continuous monitoring of physiological and movement data. Embedded sensors provide one with the capability of recording electrocardiographic data (ECG) using different electrode configurations as well as electromyographic (EMG) data. Additional sensors allow one to record thoracic and abdominal signals associated with respiration and movement data related to stretching of the garment with shoulder movements. (Courtesy of Smartex, Italy).
Figure 4
Figure 4
Smart phone based ECG monitoring system by IMEC. The Android based mobile application allows low power ECG sensors to communicate wirelessly with the phone. With increasing computational and storage capacity and ubiquitous connectivity, smart phones are expected to truly enable continuous health monitoring. (Courtesy of IMEC, The Netherlands).
Figure 5
Figure 5
Ambient sensors can unobtrusively monitor individuals in the home environment. Ambient sensors can monitor activity patterns, sleep quality, bathroom visits etc. and provide alerts to caregivers when abnormal patterns are observed. Such sensors are expected to make the home of the future smarter and safer for patients living with chronic conditions.
Figure 6
Figure 6
The ProeTEX project aims to develop smart garments for emergency responders. These smart garments integrate sensors, communication, processing and power management directly into the garment to continuously monitor emergency responders. (Courtesy of Smartex, Italy).
Figure 7
Figure 7
The Valedo low back pain therapy system by Hocoma AG combines wireless wearable motion sensors with interactive games to provide an engaging way to perform therapeutic exercises. Patients can set therapy goals, receive feedback on their performance and keep track of their progress. (Courtesy of Hocoma, Switzerland).

References

    1. Muennig PA, Glied SA. What changes in survival rates tell us about US health care. Health Affair. 2010;29:2105–2113. doi: 10.1377/hlthaff.2010.0073.
    1. Gulley S, Rasch E, Chan L. If we build it, who will come? Working-age adults with chronic health care needs and the medical home. Medical Care. 2011;49:149–155. doi: 10.1097/MLR.0b013e3182028380.
    1. Gulley SP, Rasch EK, Chan L. Ongoing coverage for ongoing care: access, utilization, and out-of-pocket spending among uninsured working-aged adults with chronic health care needs. Am J Public Health. 2011;101:368–375. doi: 10.2105/AJPH.2010.191569.
    1. Goonewardene SS, Baloch K, Sargeant I. Road traffic collisions-case fatality rate, crash injury rate, and number of motor vehicles: time trends between a developed and developing country. Am Surgeon. 2010;76:977–981.
    1. Axelrod DA, Millman D, Abecassis DD. US Health Care Reform and Transplantation. Part I: overview and impact on access and reimbursement in the private sector. Am J Transplant. 2010;10:2197–2202. doi: 10.1111/j.1600-6143.2010.03246.x.
    1. Teng X-F, Zhang Y-T, Poon CCY, Bonato P. Wearable medical systems for p-Health. IEEE Reviews in Biomedical Engineering. 2008;1:62–74.
    1. Bonato P. Wearable sensors and systems. From enabling technology to clinical applications. IEEE Eng Med Biol Mag. 2010;29:25–36.
    1. Book Health care disparities in rural areas: Selected findings from the 2004 National Healthcare Disparities Report. City: Agency for Healthcare Research and Quality; 2005. Health care disparities in rural areas: Selected findings from the 2004 National Healthcare Disparities Report. (Editor ed.^eds.)
    1. Caudill TS, Lofgren R, Jennings CD, Karpf M. Commentary: Health care reform and primary care: training physicians for tomorrow's challenges. Acad Med. 2011;86:158–160. doi: 10.1097/ACM.0b013e3182045f13.
    1. Chan L, Hart LG, Goodman DC. Geographic access to health care for rural Medicare beneficiaries. J Rural Health. 2006;22:140–146. doi: 10.1111/j.1748-0361.2006.00022.x.
    1. Reinkensmeyer DJ, Bonato P, Boninger ML, Chan L, Cowan RE, Fregly BJ, Rodgers MM. Major trends in mobility technology research and development: overview of the results of the NSF-WTEC European Study. J Neuroeng Rehabil. 2012.
    1. Brand O. Microsensor integration into systems-on-chip. Proceedings of the IEEE. 2006;94:1160–1176.
    1. DeVaul R, Sung M, Gips J, Pentland A. MIThril 2003: applications and architecture. Seventh IEEE International Symposium on Wearable Computers. 2003. pp. 4–11.
    1. ZigBee Alliance.
    1. Zhang J, Orlik PV, Sahinoglu Z, Molisch AF, Kinney P. UWB systems for wireless sensor networks. Proceedings of the IEEE. 2009;97:313–331.
    1. Want R. iPhone: smarter than the average phone. Pervasive Computing IEEE. 2010;9:6–9.
    1. Want R. When cell phones become computers. IEEE Pervasive Computing. 2009;8:2–5.
    1. Botts N, Thoms B, Noamani A, Horan TA. Cloud computing architectures for the underserved: public health cyberinfrastructures through a network of health ATMs. System Sciences (HICSS), 2010 43rd Hawaii International Conference on; 5-8 Jan. 2010. 2010. pp. 1–10.
    1. Chang HH, Chou PB, Ramakrishnan S. An Ecosystem Approach for Healthcare Services Cloud. e-Business Engineering, 2009 ICEBE '09 IEEE International Conference on; 21-23 Oct. 2009. 2009. pp. 608–612.
    1. Hoang DB, Chen L. Mobile Cloud for Assistive Healthcare (MoCAsH) Services Computing Conference (APSCC), 2010 IEEE Asia-Pacific; 6-10 Dec. 2010. 2010. pp. 325–332.
    1. Rao GSVRK, Sundararaman K, Parthasarathi J. Dhatri - A Pervasive Cloud initiative for primary healthcare services. Intelligence in Next Generation Networks (ICIN), 2010 14th International Conference on; 11-14 Oct. 2010. 2010. pp. 1–6.
    1. Asada HH, Shaltis P, Reisner A, Rhee S, Hutchinson RC. Mobile monitoring with wearable photoplethysmographic biosensors. IEEE Eng Med Biol Mag. 2003;22:28–40.
    1. Shaltis PA, Reisner A, Asada HH. Wearable, cuff-less PPG-based blood pressure monitor with novel height sensor. Conf Proc IEEE Eng Med Biol Soc. 2006;1:908–911.
    1. Corbishley P, Rodriguez-Villegas E. Breathing detection: towards a miniaturized, wearable, battery-operated monitoring system. IEEE Trans Biomed Eng. 2008;55:196–204.
    1. Barbaro M, Caboni A, Cosseddu P, Mattana G, Bonfiglio A. Active devices based on organic semiconductors for wearable applications. Information Technology in Biomedicine, IEEE Transactions on. 2010;14:758–766.
    1. Patterson JAC, McIlwraith DG, Guang-Zhong Y. A Flexible, Low Noise Reflective PPG Sensor Platform for Ear-Worn Heart Rate Monitoring. Wearable and Implantable Body Sensor Networks, 2009 BSN 2009 Sixth International Workshop on; 3-5 June 2009. 2009. pp. 286–291.
    1. Yong-Sheng Y, Yuan-Ting Z. An efficient motion-resistant method for wearable pulse oximeter. Information Technology in Biomedicine, IEEE Transactions on. 2008;12:399–405.
    1. Wood LB, Asada HH. Low Variance Adaptive Filter for Cancelling Motion Artifact in Wearable Photoplethysmogram Sensor Signals. Engineering in Medicine and Biology Society, 2007 EMBS 2007 29th Annual International Conference of the IEEE; 22-26 Aug. 2007. 2007. pp. 652–655.
    1. Lanata A, Scilingo EP, Nardini E, Loriga G, Paradiso R, De-Rossi D. Comparative evaluation of susceptibility to motion artifact in different wearable systems for monitoring respiratory rate. Information Technology in Biomedicine, IEEE Transactions on. 2010;14:378–386.
    1. Dudde R, Thomas V, Piechotta G, Hintsche R. Computer-aided continuous drug infusion: setup and test of a mobile closed-loop system for the continuous automated infusion of insulin. Information Technology in Biomedicine, IEEE Transactions on. 2006;10:395–402. doi: 10.1109/TITB.2006.864477.
    1. Morris D, Schazmann B, Wu Y, Coyle S, Brady S, Fay C, Hayes J, Lau KT, Wallace G, Diamond D. Wearable technology for bio-chemical analysis of body fluids during exercise. 2008. pp. 5741–5744.
    1. Coyle S, King-Tong L, Moyna N, O'Gorman D, Diamond D, Di Francesco F, Costanzo D, Salvo P, Trivella MG, De Rossi DE. et al.BIOTEX-Biosensing textiles for personalised healthcare management. Information Technology in Biomedicine, IEEE Transactions on. 2010;14:364–370.
    1. Curone D, Secco EL, Tognetti A, Loriga G, Dudnik G, Risatti M, Whyte R, Bonfiglio A, Magenes G. Smart garments for emergency operators: the ProeTEX project. Information Technology in Biomedicine, IEEE Transactions on. 2010;14:694–701.
    1. Wang L, Yang G-Z, Huang J, Zhang J, Yu L, Nie Z, Cumming DRS. A wireless biomedical signal interface system-on-chip for body sensor networks. Biomedical Circuits and Systems, IEEE Transactions on. 2010;4:112–117.
    1. Ahn CH, Jin-Woo C, Beaucage G, Nevin JH, Jeong-Bong L, Puntambekar A, Lee JY. Disposable smart lab on a chip for point-of-care clinical diagnostics. Proceedings of the IEEE. 2004;92:154–173. doi: 10.1109/JPROC.2003.820548.
    1. Bonato P. Advances in wearable technology and applications in physical medicine and rehabilitation. J Neuroeng Rehabil. 2005;2:2. doi: 10.1186/1743-0003-2-2.
    1. Kang HG, Mahoney DF, Hoenig H, Hirth VA, Bonato P, Hajjar I, Lipsitz LA. In situ monitoring of health in older adults: technologies and issues. J Am Geriatr Soc. 2010;58:1579–1586. doi: 10.1111/j.1532-5415.2010.02959.x.
    1. Fernández L, Blasco JM, Hernández JF. Book Wireless Sensor Networks in Ambient Intelligence. City: Univ. Politécnica de Valencia: Technologies for Health and Well-being Institute ITACA; 2009. Wireless sensor networks in ambient intelligence. (Editor ed.^eds.)
    1. Stefanov DH, Bien Z, Bang WC. The smart house for older persons and persons with physical disabilities: structure, technology arrangements, and perspectives. Neural Systems and Rehabilitation Engineering, IEEE Transactions on. 2004;12:228–250. doi: 10.1109/TNSRE.2004.828423.
    1. Jovanov E, Milenkovic A. Body area networks for ubiquitous healthcare applications: opportunities and challenges. J Med Syst. 2011;35:1245–1254. doi: 10.1007/s10916-011-9661-x.
    1. Arnrich B, Mayora O, Bardram J. Pervasive or ubiquitous healthcare? Methods Inf Med. 2010;49:65–66.
    1. TRIL Centre.
    1. TigerPlace.
    1. ORCATECH.
    1. Future Health.
    1. Gator-Tech Smart House.
    1. Technology Aware Home.
    1. MIT PlaceLab.
    1. Mathie MJ, Coster AC, Lovell NH, Celler BG, Lord SR, Tiedemann A. A pilot study of long-term monitoring of human movements in the home using accelerometry. J Telemed Telecare. 2004;10:144–151. doi: 10.1258/135763304323070788.
    1. Sazonov ES, Fulk G, Sazonova N, Schuckers S. Automatic recognition of postures and activities in stroke patients. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:2200–2203.
    1. Giansanti D, Maccioni G, Morelli S. An experience of health technology assessment in new models of care for subjects with Parkinson's disease by means of a new wearable device. Telemed J E Health. 2008;14:467–472. doi: 10.1089/tmj.2007.0078.
    1. Aziz O, Atallah L, Lo B, Elhelw M, Wang L, Yang GZ, Darzi A. A pervasive body sensor network for measuring postoperative recovery at home. Surg Innov. 2007;14:83–90. doi: 10.1177/1553350607302326.
    1. Amft O, Troster G. Recognition of dietary activity events using on-body sensors. Artif Intell Med. 2008;42:121–136. doi: 10.1016/j.artmed.2007.11.007.
    1. Amft O, Kusserow M, Troster G. Bite weight prediction from acoustic recognition of chewing. IEEE Trans Biomed Eng. 2009;56:1663–1672.
    1. Benedetti MG, Di Gioia A, Conti L, Berti L, Esposti LD, Tarrini G, Melchionda N, Giannini S. Physical activity monitoring in obese people in the real life environment. J Neuroeng Rehabil. 2009;6:47. doi: 10.1186/1743-0003-6-47.
    1. Sazonov ES, Schuckers SA, Lopez-Meyer P, Makeyev O, Melanson EL, Neuman MR, Hill JO. Toward objective monitoring of ingestive behavior in free-living population. Obesity (Silver Spring) 2009;17:1971–1975. doi: 10.1038/oby.2009.153.
    1. Merilahti J, Parkka J, Antila K, Paavilainen P, Mattila E, Malm EJ, Saarinen A, Korhonen I. Compliance and technical feasibility of long-term health monitoring with wearable and ambient technologies. J Telemed Telecare. 2009;15:302–309. doi: 10.1258/jtt.2009.081106.
    1. Sciacqua A, Valentini M, Gualtieri A, Perticone F, Faini A, Zacharioudakis G, Karatzanis I, Chiarugi F, Assimakopoulou C, Meriggi P. et al.Validation of a flexible and innovative platform for the home monitoring of heart failure patients: preliminary results. Computers in Cardiology. 2009;36:97–100.
    1. Sung M, Marci C, Pentland A. Wearable feedback systems for rehabilitation. J Neuroeng Rehabil. 2005;2:17. doi: 10.1186/1743-0003-2-17.
    1. Mundt CW, Montgomery KN, Udoh UE, Barker VN, Thonier GC, Tellier AM, Ricks RD, Darling RB, Cagle YD, Cabrol NA. et al.A multiparameter wearable physiologic monitoring system for space and terrestrial applications. IEEE Trans Inf Technol Biomed. 2005;9:382–391. doi: 10.1109/TITB.2005.854509.
    1. Anliker U, Ward JA, Lukowicz P, Troster G, Dolveck F, Baer M, Keita F, Schenker EB, Catarsi F, Coluccini L. et al.AMON: a wearable multiparameter medical monitoring and alert system. IEEE Trans Inf Technol Biomed. 2004;8:415–427. doi: 10.1109/TITB.2004.837888.
    1. Habetha J. The MyHeart project--fighting cardiovascular diseases by prevention and early diagnosis. Conf Proc IEEE Eng Med Biol Soc. 2006. pp. 6746–6749.
    1. Paradiso R, Loriga G, Taccini N. Wearable system for vital signs monitoring. Stud Health Technol Inform. 2004;108:253–259.
    1. Paradiso R, Alonso A, Cianflone D, Milsis A, Vavouras T, Malliopoulos C. Remote health monitoring with wearable non-invasive mobile system: the healthwear project. Conf Proc IEEE Eng Med Biol Soc. 2008. pp. 1699–1702.
    1. Di Rienzo M, Rizzo F, Parati G, Brambilla G, Ferratini M, Castiglioni P. MagIC system: a new textile-based wearable device for biological signal monitoring. applicability in daily life and clinical setting. Conf Proc IEEE Eng Med Biol Soc. 2005. pp. 7167–7169.
    1. Di Rienzo M, Rizzo F, Meriggi P, Bordoni B, Brambilla G, Ferratini M, Castiglioni P. Applications of a textile-based wearable system for vital signs monitoring. Conf Proc IEEE Eng Med Biol Soc. 2006. pp. 2223–2226.
    1. Life Alert Classic.
    1. AlertOne medical alert system.
    1. Automatic Fall Detection.
    1. myHalo.
    1. BrickHouse.
    1. EASY WORLS.
    1. Centre Suisse d'electronique Et de Microtechnique.
    1. Bourke AK, van de Ven PW, Chaya AE, OLaighin GM, Nelson J. Testing of a long-term fall detection system incorporated into a custom vest for the elderly. Conf Proc IEEE Eng Med Biol Soc. 2008;2008:2844–2847.
    1. Bianchi F, Redmond SJ, Narayanan MR, Cerutti S, Lovell NH. Barometric pressure and triaxial accelerometry-based falls event detection. IEEE Trans Neural Syst Rehabil Eng. 2010;18:619–627.
    1. Lanz M, Nahapetianz A, Vahdatpourz A, Kaiserx LAW, Sarrafzadeh M. SmartFall: an automatic fall detection system based on subsequence matching for the SmartCane. International Conference on Body Area Networks. 2009.
    1. Sposaro F, Tyson G. iFall: an Android application for fall monitoring and response. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:6119–6122.
    1. Dai J, Bai X, Yang Z, Shen Z, Xuan D. PerFallD: A pervasive fall detection system using mobile phones. IEEE International Conference on Pervasive Computing and Communications Workshops; Mannheim. 2010. pp. 292–297.
    1. Yavuz G, Kocak M, Ergun G, Alemdar HO, Yalcin H, Incel OD, Ersoy C. A smart phone based fall detector with online location support. International Workshop on Sensing for App Phones; Zurich, Switzerland. 2010. pp. 31–35.
    1. Fukaya K, Uchida M. Protection against impact with the ground using wearable airbags. Ind Health. 2008;46:59–65. doi: 10.2486/indhealth.46.59.
    1. Nyan MN, Tay FE, Murugasu E. A wearable system for pre-impact fall detection. J Biomech. 2008;41:3475–3481. doi: 10.1016/j.jbiomech.2008.08.009.
    1. Tamura T, Yoshimura T, Sekine M, Uchida M, Tanaka O. A wearable airbag to prevent fall injuries. IEEE Trans Inf Technol Biomed. 2009;13:910–914.
    1. Shi G, Chan CS, Li WJ, Leung K-S, Zou Y, Jin Y. Mobile human airbag system for fall protection using MEMS sensors and embedded SVM classifier. IEEE Sensors Journal. 2009;9:495–503.
    1. Bachlin M, Plotnik M, Roggen D, Maidan I, Hausdorff JM, Giladi N, Troster G. Wearable assistant for Parkinson's disease patients with the freezing of gait symptom. IEEE Trans Inf Technol Biomed. 2009;14:436–446.
    1. Smith WD, Bagley A. A miniature, wearable activity/fall monitor to assess the efficacy of mobility therapy for children with cerebral palsy during everyday living. Conf Proc IEEE Eng Med Biol Soc. 2010;2010:5030–5033.
    1. Sposaro F, Danielson J, Tyson G. iWander: an Android application for dementia patients. Conf Proc IEEE Eng Med Biol Soc. 2010;2010:3875–3878.
    1. Giansanti D, Ricci G, Maccioni G. Toward the design of a wearable system for the remote monitoring of epileptic crisis. Telemed J E Health. 2008;14:1130–1135. doi: 10.1089/tmj.2008.0034.
    1. Kramer U, Kipervasser S, Shlitner A, Kuzniecky R. A novel portable seizure detection alarm system: preliminary results. J Clin Neurophysiol. 2011.
    1. Patel S, Mancinelli C, Dalton A, Patritti B, Pang T, Schachter S, Bonato P. Detecting epileptic seizures using wearable sensors. Book Detecting epileptic seizures using wearable sensors. 2009. (Editor ed.^eds.). City.
    1. Poh MZ, Loddenkemper T, Swenson NC, Goyal S, Madsen JR, Picard RW. Continuous monitoring of electrodermal activity during epileptic seizures using a wearable sensor. Conf Proc IEEE Eng Med Biol Soc. 2010;2010:4415–4418.
    1. Dalton A, Patel S, Roy AC, Welsh M, Pang T, Schachter S, Olaighin G, Bonato P. Detecting epileptic seizures using wearable sensor technologies. Book Detecting epileptic seizures using wearable sensor technologies. 2010. pp. 73–74. (Editor ed.^eds.). City.
    1. Wood A, Stankovic J, Virone G, Selavo L, He Z, Cao Q, Doan T, Wu Y, Fang L, Stoleru R. Context-aware wireless sensor networks for assisted-living and residential monitoring. IEEE Network. 2008;22:26–33.
    1. Magenes G, Curone D, Caldani L, Secco EL. Fire fighters and rescuers monitoring through wearable sensors: The ProeTEX project. Conf Proc IEEE Eng Med Biol Soc. 2010. pp. 3594–3597.
    1. USC Rehabilitation Engineering Research Center.
    1. Rizzo A, Requejo P, Winstein CJ, Lange B, Ragusa G, Merians A, Patton J, Banerjee P, Aisen M. Virtual reality applications for addressing the needs of those aging with disability. Stud Health Technol Inform. 2011;163:510–516.
    1. Lanfermann G, te Vrugt J, Timmermans A, Bongers E, Lambert N, van Acht V. Technical Aids for Rehabilitation-TAR 2007: January 25-26, 2007 2007. Technical Aids for Rehabilitation-TAR 2007-Technical University of Berlin; 2007. Philips stroke rehabilitation exerciser.
    1. Giorgino T, Tormene P, Lorussi F, De Rossi D, Quaglini S. Sensor evaluation for wearable strain gauges in neurological rehabilitation. IEEE Trans Neural Syst Rehabil Eng. 2009;17:409–415.
    1. Giorgino T, Tormene P, Maggioni G, Pistarini C, Quaglini S. Wireless support to poststroke rehabilitation: myheart's neurological rehabilitation concept. Information Technology in Biomedicine, IEEE Transactions on. 2009;13:1012–1018.
    1. Vincent C, Deaudelin I, Robichaud L, Rousseau J, Viscogliosi C, Talbot LR, Desrosiers J. Rehabilitation needs for older adults with stroke living at home: perceptions of four populations. BMC Geriatr. 2007;7:20. doi: 10.1186/1471-2318-7-20.
    1. Manson AJ, Brown P, O'Sullivan JD, Asselman P, Buckwell D, Lees AJ. An ambulatory dyskinesia monitor. J Neurol Neurosurg Psychiatry. 2000;68:196–201. doi: 10.1136/jnnp.68.2.196.
    1. Thielgen T, Foerster F, Fuchs G, Hornig A, Fahrenberg J. Tremor in Parkinson's disease: 24-hr monitoring with calibrated accelerometry. Electromyogr Clin Neurophysiol. 2004;44:137–146.
    1. Paquet JM, Auvinet B, Chaleil D, Barrey E. Analysis of gait disorders in Parkinson's disease assessed with an accelerometer. Rev Neurol (Paris) 2003;159:786–789.
    1. Salarian A, Horak FB, Zampieri C, Carlson-Kuhta P, Nutt JG, Aminian K. iTUG, a sensitive and reliable measure of mobility. IEEE Trans Neural Syst Rehabil Eng. 2010;18:303–310.
    1. Weiss A, Herman T, Plotnik M, Brozgol M, Maidan I, Giladi N, Gurevich T, Hausdorff JM. Can an accelerometer enhance the utility of the Timed Up & Go Test when evaluating patients with Parkinson's disease? Med Eng Phys. 2010;32:119–125. doi: 10.1016/j.medengphy.2009.10.015.
    1. Salarian A, Russmann H, Vingerhoets FJ, Dehollain C, Blanc Y, Burkhard PR, Aminian K. Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring. IEEE Trans Biomed Eng. 2004;51:1434–1443. doi: 10.1109/TBME.2004.827933.
    1. Uswatte G, Foo WL, Olmstead H, Lopez K, Holand A, Simms LB. Ambulatory monitoring of arm movement using accelerometry: an objective measure of upper-extremity rehabilitation in persons with chronic stroke. Arch Phys Med Rehabil. 2005;86:1498–1501. doi: 10.1016/j.apmr.2005.01.010.
    1. Uswatte G, Giuliani C, Winstein C, Zeringue A, Hobbs L, Wolf SL. Validity of accelerometry for monitoring real-world arm activity in patients with subacute stroke: evidence from the extremity constraint-induced therapy evaluation trial. Arch Phys Med Rehabil. 2006;87:1340–1345. doi: 10.1016/j.apmr.2006.06.006.
    1. Prajapati SK, Gage WH, Brooks D, Black SE, McIlroy WE. A novel approach to ambulatory monitoring: investigation into the quantity and control of everyday walking in patients with subacute stroke. Neurorehabil Neural Repair. 2011;25:6–14. doi: 10.1177/1545968310374189.
    1. Patel S, Hughes R, Hester T, Stein J, Akay M, Dy JG, Bonato P. A novel approach to monitor rehabilitation outcomes in stroke survivors using wearable technology. Proceedings of the IEEE. 2010;98:450–461.
    1. Moy ML, Mentzer SJ, Reilly JJ. Ambulatory monitoring of cumulative free-living activity. IEEE Eng Med Biol Mag. 2003;22:89–95. doi: 10.1109/MEMB.2003.1213631.
    1. Sherrill DM, Moy ML, Reilly JJ, Bonato P. Using hierarchical clustering methods to classify motor activities of COPD patients from wearable sensor data. J Neuroeng Rehabil. 2005;2:16. doi: 10.1186/1743-0003-2-16.
    1. Atallah L, Zhang J, Lo BPL, Shrikrishna D, Kelly JL, Jackson A, Polkey MI, Yang G-Z, Hopkinson NS. Validation of an ear worn sensor for activity monitoring in COPD. Am J Respir Crit Care Med. 2010;181:A1211.
    1. Steele BG, Holt L, Belza B, Ferris S, Lakshminaryan S, Buchner DM. Quantitating physical activity in COPD using a triaxial accelerometer. Chest. 2000;117:1359–1367. doi: 10.1378/chest.117.5.1359.
    1. Belza B, Steele BG, Hunziker J, Lakshminaryan S, Holt L, Buchner DM. Correlates of physical activity in chronic obstructive pulmonary disease. Nurs Res. 2001;50:195–202. doi: 10.1097/00006199-200107000-00003.
    1. Hecht A, Ma S, Porszasz J, Casaburi R. Methodology for using long-term accelerometry monitoring to describe daily activity patterns in COPD. COPD. 2009;6:121–129. doi: 10.1080/15412550902755044.
    1. Furlanetto KC, Bisca GW, Oldemberg N, Sant'anna TJ, Morakami FK, Camillo CA, Cavalheri V, Hernandes NA, Probst VS, Ramos EM. et al.Step counting and energy expenditure estimation in patients with chronic obstructive pulmonary disease and healthy elderly: accuracy of 2 motion sensors. Arch Phys Med Rehabil. 2010;91:261–267. doi: 10.1016/j.apmr.2009.10.024.
    1. Patel SA, Benzo RP, Slivka WA, Sciurba FC. Activity monitoring and energy expenditure in COPD patients: a validation study. COPD. 2007;4:107–112. doi: 10.1080/15412550701246658.
    1. Noury N, Dittmar A, Corroy C, Baghai R, Weber JL, Blanc D, Klefstat F, Blinovska A, Vaysse S, Comet B. VTAMN - A smart clothe for ambulatory remote monitoring of physiological parameters and activity. 2004. pp. 3266–3269.
    1. Wu W, Bui A, Batalin M, Au L, Binney J, Kaiser W. MEDIC: medical embedded device for individualized care. Artif Intell Med. 2008;42:137–152. doi: 10.1016/j.artmed.2007.11.006.
    1. Haiying W, Huiru Z, Augusto JC, Martin S, Mulvenna M, Carswell W, Wallace J, Jeffers P, Taylor B, McSorley K. Monitoring and analysis of sleep pattern for people with early dementia. Bioinformatics and Biomedicine Workshops (BIBMW), 2010 IEEE International Conference on; 18-18 Dec. 2010. 2010. pp. 405–410.
    1. Jimison JB, Pavel M, Pavel J, McKanna J. Home monitoring of computer interactions for the early detection of dementia. Engineering in Medicine and Biology Society, 2004 IEMBS '04 26th Annual International Conference of the IEEE; 1-5 Sept. 2004. 2004. pp. 4533–4536.
    1. Hayes TL, Abendroth F, Adami A, Pavel M, Zitzelberger TA, Kaye JA. Unobtrusive assessment of activity patterns associated with mild cognitive impairment. Alzheimers Dement. 2008;4:395–405. doi: 10.1016/j.jalz.2008.07.004.
    1. Bonato P. Wearable sensors/systems and their impact on biomedical engineering. IEEE Eng Med Biol Mag. 2003;22:18–20.
    1. Della Toffola L, Patel S, Chen BR, Ozsecen YM, Puiatti A, Bonato P. Development of a platform to combine sensor networks and home robots to improve fall detection in the home environment. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:5331–5334.

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