Wearable motion sensors to continuously measure real-world physical activities

Current Opinion in Neurology

Volumn 26, Issue 6, 2013, Pages 602-608

  Dobkin, Bruce H ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Accelerometer; Activity monitor; Gyroscope; Mobile health; Outcome assessment; Physical activity; Signal processing; Stroke rehabilitation; Telemedicine

Indexed keywords

ACCELEROMETER; ALGORITHM; BODY MOVEMENT; CLINICAL TRIAL (TOPIC); COMPUTER PROGRAM; DISABLED PERSON; ELECTROMYOGRAPHY; ENERGY CONSUMPTION; FITNESS; GAIT; GONIOMETER; HUMAN; MAGNETOMETER; MOBILE PHONE; MOTION ANALYSIS SYSTEM; NEUROLOGIC DISEASE; OUTCOME ASSESSMENT; PATIENT CARE; PHYSICAL ACTIVITY; QUALITY OF LIFE; REMOTE SENSING; REVIEW; SELF CONCEPT; SENSOR; SIGNAL PROCESSING; SOCIAL NETWORK; SPINAL CORD INJURY; VASCULAR DISEASE; WALKING; WALKING SPEED; WIRELESS MOTION SENSOR; GAIT DISORDERS, NEUROLOGIC; MOTOR ACTIVITY; PHYSIOLOGY; PROCEDURES; TELEMEDICINE;

GAIT DISORDERS, NEUROLOGIC; HUMANS; MOTOR ACTIVITY; OUTCOME ASSESSMENT (HEALTH CARE); TELEMEDICINE; WALKING;

EID: 84892370470     PISSN: 13507540     EISSN: 14736551     Source Type: Journal    
DOI: 10.1097/WCO.0000000000000026     Document Type: Review

References (50)

1. Free C, Phillips G, Galli L, et al. The effectiveness of mobile-health technologybased health behavior change of disease management interventions for healthcare consumers: A systematic review. PLoS Med 2013; 100:e1001362.
2. Clifford G, Clifton D. Wireless technology in disease management and medicine. Annu Rev Med 2012; 63:479-492.
3. Sarasohn-Kahn J. Making sense of sensors: how new technologies can change patient care. California Healthcare Foundation. Available at http://www.chcf.org/-/media/MEDIA%20LIBRARY%20Files/PDF/M/ PDF%20MakingSenseSensors.pdf.
4. Tomlinson M, Rotheram-Borus M, Swartz L, Tsai A. Scaling up mHealth: where is the evidence? PLoS Med 2013; 10:e1001382.
5. Pal K, Eastwood S, Michie S, et al. Computer-based diabetes self-management interventioons for adults with type 2 diabetes mellitus. Cochrane Datbase Syst Rev 2013; 3:CD008776.
6. Labrique A, Vasudevan L, Chang L, Mehl G. H-pe for mHealth: more 'y' or 'o' on the horizon? Int J Med Inform 2013; 82:467-469.
7. Barkovich A, Szefler S, Olson E, Rymer W. White Paper: Scientific Vision Workshop on Diagnostics and Therapeutics. Bethesda, MD: NICHD; 2011; p. 13.
8. Dobkin B, Dorsch A. The promise of mHealth: daily activity monitoring and outcome assessments by wearable sensors. Neurorehabil Neural Repair 2011; 25:788-798.
9. Welch W, Bassett D, Thompson D, et al. Classification accuracy of the wrist-worn GENEA accelerometer. Med Sci Sports Exerc 2013; 45:2012- 2019.
10. Dinesh J, Freedson P. ActiGraph and ActiCal physical activity monitors: A peek under the hood. Med Sci Sports Exerc 2012; 44:S86-S89.
11. Gebruers N, Vanroy C, Truijen S, et al. Monitoring of physical activity after stroke: A systematic review of accelerometry-based measures. Arch Phys Med Rehabil 2010; 91:288-297.
12. Duncan P, Sullivan K, Behrman A, et al. Body-weight-supported treadmill rehabilitation program after stroke. N Engl J Med 2011; 364:2026-2036.
13. Carroll S, Greig C, Lewis S, et al. The use of pedometers in stroke survivors: Are they feasible and how well do they detect steps? Arch Phys Med Rehabil 2012; 93:466-470.
14. Hartmann A, Murer K, de Bie R, de Bruin E. Reproducibility of spatio-temporal gait parameters under different conditions in older adults using a trunk tri-axial accelerometer system. Gait Posture 2009; 30:351-355.
15. Motl R, Weikert M, Suh Y, et al. Accuracy of the Actibelt accelerometer for measuring walking speed in a controlled environment among persons with multiple sclerosis. Gait Posture 2011; 35:192-196.
16. Van Remoortel H, Giavedoni S, Raste Y, et al. Validity of activity monitors in health and chronic disease: A systematic review. Int J Behav Nutr Phys Activity 2012; 9:84.
17. Spain R, St. George R, Salarian A, et al. Body-worn motion sensors detect balance and gait deficits in people with multiple sclerosis who have normal walking speed. Gait Posture 2012; 35:573-578.
18. Hiremath S, Ding D, Farringdon J, Cooper R. Predicting energy expenditure of manual wheelchair users with spinal cord injury using a multisensor-based activity monitor. Arch Phys Med Rehabil 2012; 93:1937-1943.
19. Patel S, Park H, Bonato P, et al. A review of wearable sensors and systems with applications in rehabilitation. J Neuroeng Rehabil 2012; 9:21.
20. Dobkin B, Xu X, Batalin M, et al. Reliability and validity of bilateral ankle accelerometer algorithms for activity recognition and walking speed after stroke. Stroke 2011; 42:2246-2250.
21. Xu X, Batalin M, Kaiser W, Dobkin B. Robust hierarchical system for classification of complex human mobility characteristics in the presence of neurological disorders. In: Proceedings of 2011 International Conference on Body Sensor Networks, 23-25 May 2011, Dallas, TX. IEE Explore; 2011. pp. 65-70. doi: 10.1109/BSN.2011.23.
22. Dorsch A, Thomas S, Xu C, et al. A multicenter, international, randomized clinical trial using wireless technology to affect outcomes during acute stroke rehabilitation. Neurology 2013; 80:PO4.036.
23. Yang S, Zhang J-T, Novak A, et al. Estimation of spatio-temporal parameters for poststroke hemiparetic gait using inertial sensors. Gait Posture 2013; 37:354-358.
24. Prajapati S, Gage W, Brooks D, et al. 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.
25. Weiss A, Sharifi S, Plotnik M, et al. Towards automated, at-home assessment of mobility among patients with Parkinson's disease using a body-worn accelerometer. Neurorehabil Neural Repair 2011; 25:810-818.
26. Doherty S, Oh P. A multisensor monitoring system of human physiology and daily activities. Telemed e-Health 2012; 18:185-192.
27. Barbour V, Clark J, Connell L, et al. A reality checkpoint for Mobile Health: Three challenges to overcome. PLoS Med 2013; 10:e1001395.
28. Roos M, Rudolph K, Reisman D. The structure of walking activity in people after stroke compared with older adults without disability. Phys Ther 2012; 92:1141-1147.
29. Patel S, Hughes R, Hester T, et al. Tracking motor recovery in stroke survivors undergoing rehabilitation using wearable technology. Conf Proc IEEE Eng Med Biol Soc 2010; 2010:6858-6861.
30. Rubin M, Wellik K, Channer D, Demaerschalk B. Systematic review of teleneurology: methodology. Front Neurol 2012; 3:156.
31. Chumbler N, Quigley P, Li X, et al. Effects of telerehabilitation on physical function and disability for stroke patients. Stroke 2012; 43:2168-2174.
32. Chang W, Kim M, Huh J, et al. Effects of robot-assisted gait training on cardiopulmonary fitness in subacute stroke patients: A randomized controlled study. Neurorehabil Neural Repair 2012; 26:318-324.
33. Morone GMB, Iosa M, et al. Who may benefit from robotic-assisted gait training? A randomized clinical trial in patients with subacute stroke. Neurorehabil Neural Repair 2011; 25:636-644.
34. Daly J, Zimbelman J, Roenigk K, et al. Recovery of coordinated gait: Randomized controlled stroke trial of functional electrical stimulation (FES) versus no FES, with weight-supported treadmill and over-ground training. Neurorehabil Neural Repair 2011; 25:588-596.
35. Dobkin B, Apple D, Barbeau H, et al. Weight-supported treadmill vs overground training for walking after acute incomplete SCI. Neurology 2006; 66:484-493.
36. Alcobendas-Maestro M, Esclarin-Ruiz A, Casado-Lopez R, et al. Lokomat robotic-assisted versus overground training within 3 to 6 months of incomplete spinal cord lesion: Randomized controlled trial. Neurorehabil Neural Repair 2012; 26:1058-1063.
37. Carda S, Invernizzi M, Baricich A, et al. Robotic gait training is not superior to conventional treadmill training in Parkinson disease. Neurorehabil Neural Repair 2012; 26:1027-1034.
38. Picelli A, Melotti C, Origano F, et al. Robot-assisted gait training in patient with Parkinson disease: A randomized controlled trial. Neurorehabil Neural Repair 2012; 26:353-361.
39. Vaney C, Gattlen B, Lugon-Moulin V, et al. Robotic-assisted step training (Lokomat) not superior to equal intensity of over-ground rehabilitation in patients with multiple sclerosis. Neurorehabil Neural Repair 2012; 26:212-221.
40. Dobkin B, Duncan P. Should body weight-supported treadmill training and robotic-assistive steppers for locomotor training trot back to the starting gate? Neurorehabil Neural Repair 2012; 26:308-317.
41. Dean C, Rissel C, Sherrington C, et al. Exercise to enhance mobility and prevent falls after stroke: The community stroke club randomized trial. Neurorehabil Neural Repair 2012; 26:1046-1057.
42. Van Wijk R, Cumming T, Churilov L, et al. An early mobilization protocol successfully delivers more and earlier therapy to acute stroke patients: further results from Phase II of AVERT. Neurorehabil Neural Repair 2012; 26:20-26.
43. Cella D, Lai J, Nowinski C, et al. Neuro-QOL: brief measures of health-related quality of life for clinical research in neurology. Neurology 2012; 78:1860- 1867.
44. Van Hedel H, Dokladal P, Holtz-Boendermaker S, EM-SCI Study Group. Mismatch between investigator-determined and patient-reported independence after spinal cord injury: Consequences for rehabilitation and trials. Neurorehabil Neural Repair 2011; 25:855-864.
45. Svensson S, Menkes D, Lexchin J. Surrogate outcomes in clinical trials. JAMA Intern Med 2013; 173:611-612.
46. Nadeau S, Wu S, Dobkin B, et al. Effects of task-specific and impairmentbased training compared with usual care on functional walking ability after inpatient stroke rehabilitation: LEAPS trial. Neurorehabil Neural Repair 2013; 27:370-380.
47. Barclay-Goddard R, Lix L, Tate R, et al. Health-related quality of life after stroke: does response shift occur in self-perceived physical function? Arch Phys Med Rehabil 2011; 92:1762-1769.
48. Horn S, Gassaway J. Practice based evidence: Incorporating clinical heterogeneity and patient-reported outcomes for comparative effectiveness research. Med Care 2010; 48:S17-S22.
49. Hale L, 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; 89:1765-1771.
50. Ishikawa S, Stevens S, Kang M, Morgan D. Reliability of daily step activity monitoring in adults with incomplete spinal cord injury. J Rehabil Res Dev 2011; 48:1187-1194.