Wearable sensors for clinical applications in epilepsy, Parkinson's disease, and stroke: a mixed-methods systematic review

Dongni Johansson, Kristina Malmgren, Margit Alt Murphy, Dongni Johansson, Kristina Malmgren, Margit Alt Murphy

Abstract

Objectives: Wearable technology is increasingly used to monitor neurological disorders. The purpose of this systematic review was to synthesize knowledge from quantitative and qualitative clinical researches using wearable sensors in epilepsy, Parkinson's disease (PD), and stroke.

Methods: A systematic literature search was conducted in PubMed and Scopus spanning from 1995 to January 2017. A synthesis of the main findings, reported adherence to wearables and missing data from quantitative studies, is provided. Clinimetric properties of measures derived from wearables in laboratory, free activities in hospital, and free-living environment were also evaluated. Qualitative thematic synthesis was conducted to explore user experiences and acceptance of wearables.

Results: In total, 56 studies (50 reporting quantitative and 6 reporting qualitative data) were included for data extraction and synthesis. Among studies reporting quantitative data, 5 were in epilepsy, 21 PD, and 24 studies in stroke. In epilepsy, wearables are used to detect and differentiate seizures in hospital settings. In PD, the focus is on quantification of cardinal motor symptoms and medication-evoked adverse symptoms in both laboratory and free-living environment. In stroke upper extremity activity, walking and physical activity have been studied in laboratory and during free activities. Three analytic themes emerged from thematic synthesis of studies reporting qualitative data: acceptable integration in daily life, lack of confidence in technology, and the need to consider individualization.

Conclusions: Wearables may provide information of clinical features of interest in epilepsy, PD and stroke, but knowledge regarding the clinical utility for supporting clinical decision making remains to be established.

Keywords: Epilepsy; Parkinson’s disease; Stroke; Systematic review; Wearable sensors.

Conflict of interest statement

None of the authors report any conflicts of interest with regard to the present study.

Figures

Fig. 1
Fig. 1
Flow diagram of the systematic review selection process
Fig. 2
Fig. 2
Reported outcomes of measures derived from wearables applied in epilepsy, PD, and stroke. GTCS generalized tonic–clonic seizures, PNES pshychogenic non-epileptic seizures, PD Parkinson’s disease, Sens sensitivity, Spec specificity, COP center of pressure, ICC intraclass correlations, PSG polysomnography, OMCS optical motion capture system, ARAT the Action Research Arm Test, MAL The Motor Activity Log, FMA Fugl–Meyer Assessment, NIHSS the Nation Institutes of Health Stroke Scale, UPDRS Unified Parkinson’s Disease Rating Scale, MiniBEST Mini Balance Evaluation Systems Test, PIGD postural instability and gait disorder, UDysRS Unified Dyskinesia Rating Scale, mAIMS modified Abnormal Involuntary Movement Scale, CDRS Clinical Dyskinesia Rating Scale. *Mean value is presented; §Negative correlation is shown
Fig. 3
Fig. 3
a Adherence of continuous monitoring using wearables. b Reported missing data due to technical errors and/or insufficient time of wearing or person related reasons. Mean data is presented. #Adherence rate is shown

References

    1. Steins D, Dawes H, Esser P, Collett J. Wearable accelerometry-based technology capable of assessing functional activities in neurological populations in community settings: a systematic review. J Neuroeng Rehabil. 2014;11:36. doi: 10.1186/1743-0003-11-36.
    1. Ramgopal S, Thome-Souza S, Jackson M, Kadish NE, Sanchez Fernandez I, Klehm J, Bosl W, Reinsberger C, Schachter S, Loddenkemper T. Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy Behav. 2014;37:291–307. doi: 10.1016/j.yebeh.2014.06.023.
    1. Ozanne A, Johansson D, Hallgren Graneheim U, Malmgren K, Bergquist F, Alt Murphy M. Wearables in epilepsy and Parkinson’s disease—a focus group study. Acta Neurol Scand. 2017
    1. Richardson WS, Wilson MC, Nishikawa J, Hayward RS. The well-built clinical question: a key to evidence-based decisions. ACP J Club. 1995;123(3):A12–A13.
    1. Cooke A, Smith D, Booth A. Beyond PICO: the SPIDER tool for qualitative evidence synthesis. Qual Health Res. 2012;22(10):1435–1443. doi: 10.1177/1049732312452938.
    1. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP, Initiative S. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61(4):344–349. doi: 10.1016/j.jclinepi.2007.11.008.
    1. Vandenbroucke JP, von Elm E, Altman DG, Gotzsche PC, Mulrow CD, Pocock SJ, Poole C, Schlesselman JJ, Egger M. Strengthening the reporting of observational studies in epidemiology (STROBE): explanation and elaboration. Epidemiology. 2007;18(6):805–835. doi: 10.1097/EDE.0b013e3181577511.
    1. da Costa BR, Cevallos M, Altman DG, Rutjes AW, Egger M. Uses and misuses of the STROBE statement: bibliographic study. BMJ Open. 2011;1(1):e000048. doi: 10.1136/bmjopen-2010-000048.
    1. Folletti I, Zock JP, Moscato G, Siracusa A. Asthma and rhinitis in cleaning workers: a systematic review of epidemiological studies. J Asthma. 2014;51(1):18–28. doi: 10.3109/02770903.2013.833217.
    1. Cheng HM, Guitera P. Systematic review of optical coherence tomography usage in the diagnosis and management of basal cell carcinoma. Br J Dermatol. 2015;173(6):1371–1380. doi: 10.1111/bjd.14042.
    1. Soares NM, Leao AS, Santos JR, Monteiro GR, dos Santos JR, Thomazzi SM, Silva RJ. Systematic review shows only few reliable studies of physical activity intervention in adolescents. Sci World J. 2014;2014:206478. doi: 10.1155/2014/206478.
    1. Programme CAS (2017) CASP (Qualitative Checklists). . Accessed 29 Dec 2017
    1. Thomas J, Harden A. Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Med Res Methodol. 2008;8:45. doi: 10.1186/1471-2288-8-45.
    1. Nijsen TM, Arends JB, Griep PA, Cluitmans PJ. The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy. Epilepsy Behav. 2005;7(1):74–84. doi: 10.1016/j.yebeh.2005.04.011.
    1. Beniczky S, Polster T, Kjaer TW, Hjalgrim H. Detection of generalized tonic-clonic seizures by a wireless wrist accelerometer: a prospective, multicenter study. Epilepsia. 2013;54(4):e58–e61. doi: 10.1111/epi.12120.
    1. Velez M, Fisher RS, Bartlett V, Le S. Tracking generalized tonic-clonic seizures with a wrist accelerometer linked to an online database. Seizure. 2016;39:13–18. doi: 10.1016/j.seizure.2016.04.009.
    1. Bayly J, Carino J, Petrovski S, Smit M, Fernando DA, Vinton A, Yan B, Gubbi JR, Palaniswami MS, O’Brien TJ. Time-frequency mapping of the rhythmic limb movements distinguishes convulsive epileptic from psychogenic nonepileptic seizures. Epilepsia. 2013;54(8):1402–1408. doi: 10.1111/epi.12207.
    1. Gubbi J, Kusmakar S, Rao A, Yan B, O’Brien T, Palaniswami M. Automatic detection and classification of convulsive psychogenic non-epileptic seizures using a wearable device. IEEE J Biomed Health Inform. 2015
    1. Dunnewold RJ, Hoff JI, van Pelt HC, Fredrikze PQ, Wagemans EA, van Hilten BJ. Ambulatory quantitative assessment of body position, bradykinesia, and hypokinesia in Parkinson’s disease. J Clin Neurophysiol. 1998;15(3):235–242. doi: 10.1097/00004691-199805000-00007.
    1. Scanlon BK, Levin BE, Nation DA, Katzen HL, Guevara-Salcedo A, Singer C, Papapetropoulos S. An accelerometry-based study of lower and upper limb tremor in Parkinson’s disease. J Clin Neurosci. 2013;20(6):827–830. doi: 10.1016/j.jocn.2012.06.015.
    1. Heldman DA, Espay AJ, LeWitt PA, Giuffrida JP. Clinician versus machine: reliability and responsiveness of motor endpoints in Parkinson’s disease. Parkinsonism Relat Disord. 2014;20(6):590–595. doi: 10.1016/j.parkreldis.2014.02.022.
    1. Mancini M, Carlson-Kuhta P, Zampieri C, Nutt JG, Chiari L, Horak FB. Postural sway as a marker of progression in Parkinson’s disease: a pilot longitudinal study. Gait Posture. 2012;36(3):471–476. doi: 10.1016/j.gaitpost.2012.04.010.
    1. Mancini M, Salarian A, Carlson-Kuhta P, Zampieri C, King L, Chiari L, Horak FB. ISway: a sensitive, valid and reliable measure of postural control. J Neuroeng Rehabil. 2012;9:59. doi: 10.1186/1743-0003-9-59.
    1. Lopane G, Mellone S, Chiari L, Cortelli P, Calandra-Buonaura G, Contin M. Dyskinesia detection and monitoring by a single sensor in patients with Parkinson’s disease. Mov Disord. 2015
    1. Pulliam CL, Burack MA, Heldman DA, Giuffrida JP, Mera TO. Motion sensor dyskinesia assessment during activities of daily living. J Parkinsons Dis. 2014;4(4):609–615.
    1. Ramsperger R, Meckler S, Heger T, van Uem J, Hucker S, Braatz U, Graessner H, Berg D, Manoli Y, Serrano JA, Ferreira JJ, Hobert MA, Maetzler W, team S-Ps Continuous leg dyskinesia assessment in Parkinson’s disease-clinical validity and ecological effect. Parkinsonism Relat Disord. 2016;26:41–46. doi: 10.1016/j.parkreldis.2016.02.007.
    1. Mera TO, Burack MA, Giuffrida JP. Objective motion sensor assessment highly correlated with scores of global levodopa-induced dyskinesia in Parkinson’s disease. J Parkinsons Dis. 2013;3(3):399–407.
    1. Horne MK, McGregor S, Bergquist F. An objective fluctuation score for Parkinson’s disease. PLoS One. 2015;10(4):e0124522. doi: 10.1371/journal.pone.0124522.
    1. Rodriguez-Molinero A, Sama A, Perez-Martinez DA, Perez Lopez C, Romagosa J, Bayes A, Sanz P, Calopa M, Galvez-Barron C, de Mingo E, Rodriguez Martin D, Gonzalo N, Formiga F, Cabestany J, Catala A. Validation of a portable device for mapping motor and gait disturbances in Parkinson’s disease. JMIR mHealth uHealth. 2015;3(1):e9. doi: 10.2196/mhealth.3321.
    1. Maglione JE, Liu L, Neikrug AB, Poon T, Natarajan L, Calderon J, Avanzino JA, Corey-Bloom J, Palmer BW, Loredo JS, Ancoli-Israel S. Actigraphy for the assessment of sleep measures in Parkinson’s disease. Sleep. 2013;36(8):1209–1217. doi: 10.5665/sleep.2888.
    1. Lord S, Rochester L, Baker K, Nieuwboer A. Concurrent validity of accelerometry to measure gait in Parkinsons Disease. Gait Posture. 2008;27(2):357–359. doi: 10.1016/j.gaitpost.2007.04.001.
    1. Esser P, Dawes H, Collett J, Feltham MG, Howells K. Validity and inter-rater reliability of inertial gait measurements in Parkinson’s disease: a pilot study. J Neurosci Methods. 2012;205(1):177–181. doi: 10.1016/j.jneumeth.2012.01.005.
    1. Yungher DA, Morris TR, Dilda V, Shine JM, Naismith SL, Lewis SJ, Moore ST. Temporal characteristics of high-frequency lower-limb oscillation during freezing of gait in Parkinson’s disease. Parkinsons Dis. 2014;2014:606427.
    1. Morris TR, Cho C, Dilda V, Shine JM, Naismith SL, Lewis SJ, Moore ST. A comparison of clinical and objective measures of freezing of gait in Parkinson’s disease. Parkinsonism Relat Disord. 2012;18(5):572–577. doi: 10.1016/j.parkreldis.2012.03.001.
    1. Weiss A, Herman T, Giladi N, Hausdorff JM. Objective assessment of fall risk in Parkinson’s disease using a body-fixed sensor worn for 3 days. PLoS One. 2014;9(5):e96675. doi: 10.1371/journal.pone.0096675.
    1. Iluz T, Gazit E, Herman T, Sprecher E, Brozgol M, Giladi N, Mirelman A, Hausdorff JM. Automated detection of missteps during community ambulation in patients with Parkinson’s disease: a new approach for quantifying fall risk in the community setting. J Neuroeng Rehabil. 2014;11:48. doi: 10.1186/1743-0003-11-48.
    1. Cavanaugh JT, Ellis TD, Earhart GM, Ford MP, Foreman KB, Dibble LE. Capturing ambulatory activity decline in Parkinson’s disease. J Neurol Phys Ther. 2012;36(2):51–57. doi: 10.1097/NPT.0b013e318254ba7a.
    1. Skidmore FM, Mackman CA, Pav B, Shulman LM, Garvan C, Macko RF, Heilman KM. Daily ambulatory activity levels in idiopathic Parkinson disease. J Rehabil Res Dev. 2008;45(9):1343–1348. doi: 10.1682/JRRD.2008.01.0002.
    1. Nero H, Benka Wallen M, Franzen E, Conradsson D, Stahle A, Hagstromer M. Objectively assessed physical activity and its association with balance, physical function and dyskinesia in Parkinson’s disease. J Parkinsons Dis. 2016;6(4):833–840. doi: 10.3233/JPD-160826.
    1. Gebruers N, Truijen S, Engelborghs S, Nagels G, Brouns R, De Deyn PP. Actigraphic measurement of motor deficits in acute ischemic stroke. Cerebrovasc Dis. 2008;26(5):533–540. doi: 10.1159/000160210.
    1. Gebruers N, Truijen S, Engelborghs S, De Deyn PP. Predictive value of upper-limb accelerometry in acute stroke with hemiparesis. J Rehabil Res Dev. 2013;50(8):1099–1106. doi: 10.1682/JRRD.2012.09.0166.
    1. Le Heron C, Fang K, Gubbi J, Churilov L, Palaniswami M, Davis S, Yan B. Wireless accelerometry is feasible in acute monitoring of upper limb motor recovery after ischemic stroke. Cerebrovasc Dis. 2014;37(5):336–341. doi: 10.1159/000360808.
    1. Thrane G, Emaus N, Askim T, Anke A. Arm use in patients with subacute stroke monitored by accelerometry: association with motor impairment and influence on self-dependence. J Rehabil Med. 2011;43(4):299–304. doi: 10.2340/16501977-0676.
    1. Lang CE, Wagner JM, Edwards DF, Dromerick AW. Upper extremity use in people with hemiparesis in the first few weeks after stroke. J Neurol Phys Ther. 2007;31(2):56–63. doi: 10.1097/NPT.0b013e31806748bd.
    1. de Niet M, Bussmann JB, Ribbers GM, Stam HJ. The stroke upper-limb activity monitor: its sensitivity to measure hemiplegic upper-limb activity during daily life. Arch Phys Med Rehabil. 2007;88(9):1121–1126. doi: 10.1016/j.apmr.2007.06.005.
    1. van der Pas SC, Verbunt JA, Breukelaar DE, van Woerden R, Seelen HA. Assessment of arm activity using triaxial accelerometry in patients with a stroke. Arch Phys Med Rehabil. 2011;92(9):1437–1442. doi: 10.1016/j.apmr.2011.02.021.
    1. Shim S, Kim H, Jung J. Comparison of upper extremity motor recovery of stroke patients with actual physical activity in their daily lives measured with accelerometers. J Phys Ther Sci. 2014;26(7):1009–1011. doi: 10.1589/jpts.26.1009.
    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(7):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(10):1340–1345. doi: 10.1016/j.apmr.2006.06.006.
    1. Michielsen ME, Selles RW, Stam HJ, Ribbers GM, Bussmann JB. Quantifying nonuse in chronic stroke patients: a study into paretic, nonparetic, and bimanual upper-limb use in daily life. Arch Phys Med Rehabil. 2012;93(11):1975–1981. doi: 10.1016/j.apmr.2012.03.016.
    1. Urbin MA, Waddell KJ, Lang CE. Acceleration metrics are responsive to change in upper extremity function of stroke survivors. Arch Phys Med Rehabil. 2015;96(5):854–861. doi: 10.1016/j.apmr.2014.11.018.
    1. Mudge S, Stott NS. Test–retest reliability of the StepWatch activity monitor outputs in individuals with chronic stroke. Clin Rehabil. 2008;22(10–11):871–877. doi: 10.1177/0269215508092822.
    1. Mudge S, Stott NS, Walt SE. Criterion validity of the StepWatch activity monitor as a measure of walking activity in patients after stroke. Arch Phys Med Rehabil. 2007;88(12):1710–1715. doi: 10.1016/j.apmr.2007.07.039.
    1. Fulk GD, Combs SA, Danks KA, Nirider CD, Raja B, Reisman DS. Accuracy of 2 activity monitors in detecting steps in people with stroke and traumatic brain injury. Phys Ther. 2014;94(2):222–229. doi: 10.2522/ptj.20120525.
    1. Askim T, Bernhardt J, Churilov L, Fredriksen KR, Indredavik B. Changes in physical activity and related functional and disability levels in the first 6 months after stroke: a longitudinal follow-up study. J Rehabil Med. 2013;45(5):423–428. doi: 10.2340/16501977-1137.
    1. Hale LA, Pal J, Becker I. Measuring free-living physical activity in adults with and without neurologic dysfunction with a triaxial accelerometer. Arch Phys Med Rehabil. 2008;89(9):1765–1771. doi: 10.1016/j.apmr.2008.02.027.
    1. Rand D, Eng JJ, Tang PF, Jeng JS, Hung C. How active are people with stroke? Use of accelerometers to assess physical activity. Stroke. 2009;40(1):163–168. doi: 10.1161/STROKEAHA.108.523621.
    1. Haeuber E, Shaughnessy M, Forrester LW, Coleman KL, Macko RF. Accelerometer monitoring of home- and community-based ambulatory activity after stroke. Arch Phys Med Rehabil. 2004;85(12):1997–2001. doi: 10.1016/j.apmr.2003.11.035.
    1. Vanroy C, Vissers D, Cras P, Beyne S, Feys H, Vanlandewijck Y, Truijen S. Physical activity monitoring in stroke: senseWear Pro2 activity accelerometer versus Yamax Digi-Walker SW-200 pedometer. Disabil Rehabil. 2014;36(20):1695–1703. doi: 10.3109/09638288.2013.859307.
    1. Sanchez MC, Bussmann J, Janssen W, Horemans H, Chastin S, Heijenbrok M, Stam H. Accelerometric assessment of different dimensions of natural walking during the first year after stroke: recovery of amount, distribution, quality and speed of walking. J Rehabil Med. 2015
    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(1):6–14. doi: 10.1177/1545968310374189.
    1. Tieges Z, Mead G, Allerhand M, Duncan F, van Wijck F, Fitzsimons C, Greig C, Chastin S. Sedentary behavior in the first year after stroke: a longitudinal cohort study with objective measures. Arch Phys Med Rehabil. 2015;96(1):15–23. doi: 10.1016/j.apmr.2014.08.015.
    1. Barak S, Wu SS, Dai Y, Duncan PW, Behrman AL. Adherence to accelerometry measurement of community ambulation poststroke. Phys Ther. 2014;94(1):101–110. doi: 10.2522/ptj.20120473.
    1. Fisher JM, Hammerla NY, Rochester L, Andras P, Walker RW. Body-worn sensors in Parkinson’s disease: evaluating their acceptability to patients. Telemed J E Health. 2016;22(1):63–69. doi: 10.1089/tmj.2015.0026.
    1. Simone LK, Sundarrajan N, Luo X, Jia Y, Kamper DG. A low cost instrumented glove for extended monitoring and functional hand assessment. J Neurosci Methods. 2007;160(2):335–348. doi: 10.1016/j.jneumeth.2006.09.021.
    1. Zhao Y, Heida T, van Wegen EE, Bloem BR, van Wezel RJ. E-health support in people with Parkinson’s disease with smart glasses: a survey of user requirements and expectations in the Netherlands. J Parkinsons Dis. 2015
    1. Hoppe C, Feldmann M, Blachut B, Surges R, Elger CE, Helmstaedter C. Novel techniques for automated seizure registration: Patients’ wants and needs. Epilepsy Behav. 2015;52(Pt A):1–7. doi: 10.1016/j.yebeh.2015.08.006.
    1. Mountain G, Wilson S, Eccleston C, Mawson S, Hammerton J, Ware T, Zheng H, Davies R, Black N, Harris N, Stone T, Hu H. Developing and testing a telerehabilitation system for people following stroke: issues of usability. J Eng Des. 2010;21(2):223–236. doi: 10.1080/09544820903333792.
    1. Cancela J, Pastorino M, Tzallas AT, Tsipouras MG, Rigas G, Arredondo MT, Fotiadis DI. Wearability assessment of a wearable system for Parkinson’s disease remote monitoring based on a body area network of sensors. Sensors (Basel) 2014;14(9):17235–17255. doi: 10.3390/s140917235.
    1. Kubota KJ, Chen JA, Little MA. Machine learning for large-scale wearable sensor data in Parkinson’s disease: concepts, promises, pitfalls, and futures. Mov Disord. 2016;31(9):1314–1326. doi: 10.1002/mds.26693.
    1. Bustren EL, Sunnerhagen KS, Alt Murphy M. Movement kinematics of the ipsilesional upper extremity in persons with moderate or mild stroke. Neurorehabil Neural Repair. 2017;31(4):376–386. doi: 10.1177/1545968316688798.
    1. Poh MZ, Loddenkemper T, Reinsberger C, Swenson NC, Goyal S, Sabtala MC, Madsen JR, Picard RW. Convulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor. Epilepsia. 2012;53(5):e93–e97. doi: 10.1111/j.1528-1167.2012.03444.x.
    1. Cuppens K, Karsmakers P, Van de Vel A, Bonroy B, Milosevic M, Luca S, Croonenborghs T, Ceulemans B, Lagae L, Van Huffel S, Vanrumste B. Accelerometry-based home monitoring for detection of nocturnal hypermotor seizures based on novelty detection. IEEE J Biomed Health Inform. 2014;18(3):1026–1033. doi: 10.1109/JBHI.2013.2285015.
    1. Jory C, Shankar R, Coker D, McLean B, Hanna J, Newman C. Safe and sound? A systematic literature review of seizure detection methods for personal use. Seizure. 2016;36:4–15. doi: 10.1016/j.seizure.2016.01.013.
    1. Godinho C, Domingos J, Cunha G, Santos AT, Fernandes RM, Abreu D, Goncalves N, Matthews H, Isaacs T, Duffen J, Al-Jawad A, Larsen F, Serrano A, Weber P, Thoms A, Sollinger S, Graessner H, Maetzler W, Ferreira JJ. A systematic review of the characteristics and validity of monitoring technologies to assess Parkinson’s disease. J Neuroeng Rehabil. 2016;13:24. doi: 10.1186/s12984-016-0136-7.
    1. Noorkoiv M, Rodgers H, Price CI. Accelerometer measurement of upper extremity movement after stroke: a systematic review of clinical studies. J Neuroeng Rehabil. 2014;11:144. doi: 10.1186/1743-0003-11-144.
    1. Espay AJ, Bonato P, Nahab FB, Maetzler W, Dean JM, Klucken J, Eskofier BM, Merola A, Horak F, Lang AE, Reilmann R, Giuffrida J, Nieuwboer A, Horne M, Little MA, Litvan I, Simuni T, Dorsey ER, Burack MA, Kubota K, Kamondi A, Godinho C, Daneault JF, Mitsi G, Krinke L, Hausdorff JM, Bloem BR, Papapetropoulos S, Movement Disorders Society Task Force on T Technology in Parkinson’s disease: Challenges and opportunities. Mov Disord. 2016;31(9):1272–1282. doi: 10.1002/mds.26642.
    1. Silva de Lima AL, Evers LJW, Hahn T, Bataille L, Hamilton JL, Little MA, Okuma Y, Bloem BR, Faber MJ. Freezing of gait and fall detection in Parkinson’s disease using wearable sensors: a systematic review. J Neurol. 2017;264(8):1642–1654. doi: 10.1007/s00415-017-8424-0.

Source: PubMed

3
Prenumerera