Abstract
Studies have shown that patients who practice functional movements at home in conjunction with outpatient therapy show higher improvement in motor recovery. However, patients are not qualified to monitor or assess their own condition that must be reported back to the clinician. Therefore, there is a need to transmit physiological data to clinicians from patients in their home environment. This paper presents a review of wearable technology for in-home health monitoring, assessment, and rehabilitation of patients with brain and spinal cord injuries.
References
Abbasi-kesbi, R., Nikfarjam, A., and Memarzadeh-Tehran, H. (2017). A patient-centric sensory system for in-home rehabilitation. IEEE Sens. J. 17, 524–533.10.1109/JSEN.2016.2631464Search in Google Scholar
Adeli, H. and Hung, S.L. (1994). Machine Learning – Neural Networks, Genetic Algorithms, and Fuzzy Systems (New York, NY, USA: John Wiley and Sons).Search in Google Scholar
Ahmadlou, M. and Adeli, H. (2010). Enhanced probabilistic neural network with local decision circles: a robust classifier. Integr. Comput. Aided. Eng. 17, 197–210.10.3233/ICA-2010-0345Search in Google Scholar
Amezquita-Sanchez, J. and Adeli, H. (2015a). Synchrosqueezed wavelet transform-fractality model for locating, detecting, and quantifying damage in smart highrise building structures. Smart Mater. Struct. 24, 65034.10.1088/0964-1726/24/6/065034Search in Google Scholar
Amezquita-Sanchez, J.P. and Adeli, H. (2015b). A new music-empirical wavelet transform methodology for time-frequency analysis of noisy nonlinear and non-stationary signals. Digit. Signal Process. A Rev. J. 45, 55–68.10.1016/j.dsp.2015.06.013Search in Google Scholar
Bonora, G., Mancini, M., Carpinella, I., Chiari, L., Horak, F.B., and Ferrarin, M. (2017). Gait initiation is impaired in subjects with Parkinson’s disease in the off state: evidence from the analysis of the anticipatory postural adjustments through wearable inertial sensors. Gait Posture 51, 218–221.10.1016/j.gaitpost.2016.10.017Search in Google Scholar PubMed PubMed Central
Brodie, M.A.D., Canning, C.G., Beijer, T.R., and Lord, S.R. (2015). Uncontrolled head oscillations in people with Parkinson’s disease may reflect an inability to respond to perturbations while walking. Physiol. Meas. 36, 873–881.10.1088/0967-3334/36/5/873Search in Google Scholar PubMed
Castillo, E., Peteiro-Barral, D., Guijarro Berdinas, B., and Fontenla-Romero, O. (2015). Distributed one-class support vector machine. Int. J. Neural Syst. 25, 1550029.10.1142/S012906571550029XSearch in Google Scholar PubMed
Choi, S. and Jiang, Z. (2006). A novel wearable sensor device with conductive fabric and pvdf film for monitoring cardiorespiratory signals. Sensors Actuators A 128, 317–326.10.1016/j.sna.2006.02.012Search in Google Scholar
Donos, C., Duemoelmann, M., and Schulze-Bonhage, A. (2015). Early seizure detection algorithm based on intractable eeg and random forest classification. Int. J. Neural Syst. 25, 1550023.10.1142/S0129065715500239Search in Google Scholar PubMed
Ferrari, A., Ginis, P., Hardegger, M., Casamassima, F., Rocchi, L., and Chiari, L. (2016). A mobile kalman-filter based solution for the real-time estimation of spatio-temporal gait parameters. IEEE Trans. Neural Syst. Rehabil. Eng. 24, 764–773.10.1109/TNSRE.2015.2457511Search in Google Scholar PubMed
Finni, T., Hu, M., Kettunen, P., Vilavuo, T., and Cheng, S. (2007). Measurement of emg activity with textile electrodes embedded into clothing. Physiol. Meas. 28, 1405–1419.10.1088/0967-3334/28/11/007Search in Google Scholar PubMed
George, S.H., Rafiei, M.H., Borstad, A., Adeli, H., and Gauthier, L. (2017a). Gross motor ability predicts response to upper extremity rehabilitation in chronic stroke. Behav. Brain Res. 333, 314–322.10.1016/j.bbr.2017.07.002Search in Google Scholar PubMed PubMed Central
George, S.H., Rafiei, M.H., Borstad, A., Gauthier, L., Buford, J.A., and Adeli, H. (2017b). Computer-aided prediction of extent of motor recovery following constraint-induced movement therapy in chronic stroke. Behav. Brain Res. 327, 191–199.10.1016/j.bbr.2017.03.012Search in Google Scholar PubMed
Gouwanda, D., Gopalai, A.A., and Khoo, B.H. (2016). A low cost alternative to monitor human gait temporal parameters – wearable wireless gyroscope. IEEE Sens. J. 16, 9029–9035.10.1109/JSEN.2016.2616163Search in Google Scholar
Hirschauer, T.J., Adeli, H., and Buford, J.A. (2015). Computer-aided diagnosis of Parkinson’s disease using enhanced probabilistic neural network. J. Med. Syst. 39, 179–191.10.1007/s10916-015-0353-9Search in Google Scholar PubMed
Huo, X. and Ghovanloo, M. (2010). Evaluation of a wireless wearable tongue-computer interface by individuals with high-level spinal cord injuries. J. Neural Eng. 7, 26008.10.1088/1741-2560/7/2/026008Search in Google Scholar PubMed PubMed Central
Huo, X. and Ghovanloo, M. (2012). Tongue drive: a wireless tongue-operated means for people with severe disabilities to communicate their intentions. IEEE Commun. Mag. 50, 128–135.10.1109/MCOM.2012.6316786Search in Google Scholar
Huo, X., Park, H., Kim, J., and Ghovanloo, M. (2013). A dual-mode human computer interface combining speech and tongue motion for people with severe disabilities. IEEE Trans. Neural Syst. Rehabil. Eng. 21, 979–991.10.1109/TNSRE.2013.2248748Search in Google Scholar PubMed PubMed Central
Li, G., Geng, Y., Tao, D., and Zhou, P. (2011). Performance of electromyography recorded using textile electrodes in classifying arm movements. Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, 4243–4246.Search in Google Scholar
Maglogiannis, I., Ioannou, C., Spyroglou, G., and Tsanakas, P. (2016). Fall detecting and activity identification using wearable and handheld devices. Integr. Comput. Aided Eng. 23, 161–172.10.3233/ICA-150509Search in Google Scholar
Mancini, M., Chiari, L., Holmstrom, L., Salarian, A., and Horak, F.B. (2016). Validity and reliability of an imu-based method to detect apas prior to gait initiation. Gait Posture 43, 125–131.10.1016/j.gaitpost.2015.08.015Search in Google Scholar PubMed PubMed Central
Mariani, B., Jiménez, M.C., Vingerhoets, F.J.G., and Aminian, K. (2013). On-shoe wearable sensors for gait and turning assessment of patients with Parkinson’s disease. IEEE Trans. Biomed. Eng. 60, 155–158.10.1109/TBME.2012.2227317Search in Google Scholar PubMed
Masse, F., Gonzenbach, R., Paraschiv-Ionescu, A., Luft, A., and Aminian, K. (2016). Wearable barometric pressure sensor to improve postural transition recognition of mobility – impaired stroke patients. IEEE Trans. Neural Syst. Rehabil. Eng. 24, 1210–1217.10.1109/TNSRE.2016.2532844Search in Google Scholar PubMed
Mayo, N., Wood-Dauphinee, S., Côté, R., Gayton, D., Carlton, J., Buttery, J., and Tamblyn, R. (2000). There’s no place like home: an evaluation of early supported discharge for stroke. Stroke 31, 1016–1023.10.1161/01.STR.31.5.1016Search in Google Scholar PubMed
Mazilu, S., Calatroni, A., Gazit, E., Mirelman, A., Hausdorff, J.M., and Troster, G. (2015). Prediction of freezing of gait in Parkinson ’s from physiological wearables: an exploratory study. IEEE J. Biomed. Heal. Informat. 19, 1843–1854.10.1109/JBHI.2015.2465134Search in Google Scholar PubMed
Mazomenos, E.B., Biswas, D., Cranny, A., Rajan, A., Maharatna, K., Achner, J., Klemke, J., Jobges, M., Ortmann, S., and Langendorfer, P. (2016). Detecting elementary arm movements by tracking upper limb joint angles with marg sensors. IEEE J. Biomed. Heal. Informatics 20, 1088–1099.10.1109/JBHI.2015.2431472Search in Google Scholar PubMed
McLeod, A., Bochniewicz, E.M., Lum, P.S., Holley, R.J., Emmer, G., and Dromerick, A.W. (2016). Using wearable sensors and machine learning models to separate functional upper extremity use from walking-associated arm movements. Arch. Phys. Med. Rehabil. 97, 224–231.10.1016/j.apmr.2015.08.435Search in Google Scholar PubMed
Mirzaei, G. and Adeli, H. (2016). Resting state functional magnetic resonance image processing techniques in stroke studies. Rev. Neurosci. 27, 871–885.10.1515/revneuro-2016-0052Search in Google Scholar PubMed
Palomo, E.J. and Lopez-Rubio, E. (2016). Learning topologies with the growing neural forest. Int. J. Neural Syst. 26, 1650019.10.1142/S0129065716500192Search in Google Scholar PubMed
Patel, S., Hughes, R., and Hester, T. (2010). A novel approach to monitor rehabilitation outcomes in stroke survivors using wearable technology. Proc. IEEE 98, 450–461.10.1109/JPROC.2009.2038727Search in Google Scholar
Rafiei, M.H. and Adeli, H. (2017). A new neural dynamic classification algorithm. IEEE Trans. Neural Networks Learn. Syst. 28, 12.10.1109/TNNLS.2017.2682102Search in Google Scholar PubMed
Salarian, A., Russmann, H., Vingerhoets, F.J.G., Dehollain, C., Blanc, Y., Burkhard, P.R., and Aminian, K. (2004). Gait assessment in Parkinson’s disease: toward an ambulatory system for long-term monitoring. IEEE Trans. Biomed. Eng. 51, 1434–1443.10.1109/TBME.2004.827933Search in Google Scholar PubMed
Singh, G., Nelson, A., Lu, S., Robucci, R., Patel, C., and Banerjee, N. (2016). Event-driven low-power gesture recognition using differential capacitance. IEEE Sens. J. 16, 4955–4967.10.1109/JSEN.2016.2530805Search in Google Scholar
Sringean, J., Taechalertpaisarn, P., Thanawattano, C., and Bhidayasiri, R. (2016). How well do Parkinson’s disease patients turn in bed? quantitative analysis of nocturnal hypokinesia using multisite wearable inertial sensors. Park. Relat. Disord. 23, 10–16.10.1016/j.parkreldis.2015.11.003Search in Google Scholar PubMed
Tao, D., Zhang, H., Wu, Z., and Li, G. (2012). Real-time performance of textile electrodes in electromyogram pattern-recognition based prosthesis control. Proc. IEEE-EMBS Int. Conf. Biomed. Heal. Informatics Glob. Gd. Chall. Heal. Informatics, BHI 2012 25, 487–490.10.1109/BHI.2012.6211624Search in Google Scholar
Taylor-Piliae, R.E., Mohler, M.J., Najafi, B., and Coull, B.M. (2016). Objective fall risk detection in stroke survivors using wearable sensor technology: a feasibility study. Top. Stroke Rehabil. 23, 393–399.10.1179/1074935715Z.00000000059Search in Google Scholar PubMed
Tikkanen, O., Hu, M., Vilavuo, T., Tolvanen, P., Cheng, S., and Finni, T. (2012). Ventilatory threshold during incremental running can be estimated using emg shorts. Physiol. Meas. 33, 603–614.10.1088/0967-3334/33/4/603Search in Google Scholar PubMed
Tikkanen, O., Karkkainen, S., Haakana, P., Kallinen, M., Pullinen, T., and Finni, T. (2014). Emg, heart rate, and accelerometer as estimators of energy expenditure in locomotion. Med. Sci. Sports Exerc. 46, 1831–1839.10.1249/MSS.0000000000000298Search in Google Scholar PubMed
Vahabi, Z., Amirfattahi, R., Ghassemi, F., and Shayegh, F. (2015). Online epileptic seizure prediction using wavelet-based bi-phase correlation of electrical signal tomography. Int. J. Neural Syst. 25, 1550028.10.1142/S0129065715500288Search in Google Scholar PubMed
Villar, J.R., Chira, C., Sedano, J., González, S., and Trejo, J.M. (2015a). A hybrid intelligent recognition system for the early detection of strokes. Integr. Comput. Aided Eng. 22, 215–227.10.3233/ICA-150488Search in Google Scholar
Villar, J.R., González, S., Sedano, J., Chira, C., and Trejo-Gabriel-Gala, J.M. (2015b). Improving human activity recognition and its application in early stroke diagnosis. Int. J. Neural Syst. 25, 1450036.10.1142/S0129065714500361Search in Google Scholar PubMed
Wolf, S., McJunkin, J., Swanson, M., and Weiss, P. (2006). Pilot normative database for the wolf motor function test. Phys. Med. Rehabil. 87, 443–445.10.1016/j.apmr.2005.10.006Search in Google Scholar PubMed
Wüest, S., Massé, F., Aminian, K., and Gonzenbach, R. (2016). Reliability and validity of the inertial sensor-based timed “up and go” test in individuals affected by stroke. J. Rehabil. Res. Dev. 53, 599–610.10.1682/JRRD.2015.04.0065Search in Google Scholar PubMed
Xu, J., Song, L., Xu, J.Y., Pottie, G.J., and van der Schaar, M. (2016). Personalized active learning for activity classification using wireless wearable sensors. IEEE J. Sel. Top. Signal Process. 10, 865–876.10.1109/JSTSP.2016.2553648Search in Google Scholar
Yu, L., Xiong, D., Guo, L., and Wang, J. (2016). A remote quantitative fugl-meyer assessment framework for stroke patients based on wearable sensor networks. Comput. Methods Programs Biomed. 128, 100–110.10.1016/j.cmpb.2016.02.012Search in Google Scholar PubMed
Yuen, A.C., Bakir, A.A., Rajdi, N.N.Z.M., Lam, C.L., Saleh, S.M., and Wicaksono, D.H.B. (2014). Proprioceptive sensing system for therapy assessment using cotton fabric-based biomedical microelectromechanical system. IEEE Sens. J. 14, 2872–2880.10.1109/JSEN.2014.2319779Search in Google Scholar
Yuvaraj, R., Murugappan, M., Sundaraj, K., Omar, M.I., Ibrahim, N.M., Mohamad, K., Palaniappan, R., Acharya, U.R., Adeli, H., and Mesquita, E. (2016). Brain functional connectivity patterns for emotional state classification in Parkinson’s disease patients without dementia. Behav. Brain Res. 298, 248–260.10.1016/j.bbr.2015.10.036Search in Google Scholar PubMed
Zhang, H., Tian, L., Zhang, L., and Li, G. (2013). Using textile electrode emg for prosthetic movement identification in transradial amputees. 2013 IEEE Int. Conf. Body Sens. Networks, BSN 2013, 1–5.10.1109/BSN.2013.6575510Search in Google Scholar
Zhang, Y. and Zhou, W. (2015). Multifractal analysis and relevance vector machine-based automatic seizure detection in intracranial. Int. J. Neural Syst. 25, 1550020.10.1142/S0129065715500203Search in Google Scholar PubMed
Zhang, Z., Fang, Q., and Gu, X. (2016). Objective assessment of upper limb mobility for post-stroke rehabilitation. IEEE Trans. Biomed. Eng. 63, 859–868.10.1109/TBME.2015.2477095Search in Google Scholar PubMed
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