1. Rand, D, Eng, JJ. Disparity between functional recovery and daily use of the upper and lower extremities during subacute stroke rehabilitation. Neurorehabil Neural Repair 2012; 26: 76–84.
Google Scholar | SAGE Journals | ISI2. Baniña, MC, Mullick, AA, McFadyen, BJ, et al. Upper limb obstacle avoidance behavior in individuals with stroke. Neurorehabil Neural Repair 2017; 31: 133–146.
Google Scholar | SAGE Journals | ISI3. Andrews, K, Steward, JEAN. Stroke recovery: HE can but does he? Rheumatol Rehabil 1979; 18: 43–48.
Google Scholar | Crossref | Medline4. van Meulen, FB, Klaassen, B, Held, J, et al. Objective evaluation of the quality of movement in daily life after stroke. Front Bioeng Biotechnol 2015; 3: 210.
Google Scholar | Medline5. World Health Organization. International classification of functioning, disability and health, https://books.google.com/books?hl=en&lr=&id=SWFQDXyU-rcC&oi=fnd&pg=PR5&ots=G9JMpzw-Jw&sig=mzrYA5lung-DLSGQhB0H_zh8Z_Y (2001, accessed 16 March 2021).
Google Scholar6. Taub, E, Uswatte, G, Mark, VW, et al. Method for enhancing real-world use of a more affected arm in chronic stroke: transfer package of constraint-induced movement therapy. Stroke 2013; 44: 1383–1388.
Google Scholar | Crossref | Medline | ISI7. Taub, E, McCulloch, K, Uswatte, et al. Motor Activity Log (MAL) Manual. UAB CI Therapy Research Group. Available at: http://www.uab.edu/citherapy/images/pdf_files/CIT_Training_MAL_manual.pdf (accessed 2018)
Google Scholar8. Uswatte, G, Taub, E. Implications of the learned nonuse formulation for measuring rehabilitation outcomes: lessons from constraint-induced movement therapy. Rehabil Psychol 2005; 50: 34–42.
Google Scholar | Crossref | ISI9. Wade, DT. Measurement in neurological rehabilitation. Curr Opin Neurol Neurosurg 1992; 5: 682–686.
Google Scholar | Medline10. Han, CE, Kim, S, Chen, S, et al. Quantifying arm non-use in individuals post-stroke. Growth (Lakeland) 2008; 23: 1–7.
Google Scholar11. Parker, J, Powell, L, Mawson, S. Effectiveness of upper limb wearable technology for improving activity and participation in adult stroke survivors: systematic review. J Med Internet Res 2020; 22: e15981.
Google Scholar | Crossref | Medline12. Wang, Q, Markopoulos, P, Yu, B, et al. Interactive wearable systems for upper body rehabilitation: a systematic review. J Neuroeng Rehabil 2017; 14: 20.
Google Scholar | Crossref | Medline13. Rubio, BB, Lathe, A, Duarte, E, et al. A wearable bracelet device for promoting arm use in stroke patients. In: Proceedings of the 3rd international congress on neurotechnology, electronics and informatics. Portugal: SCITEPRESS – Science and Technology Publications, pp. 24–31.
Google Scholar14. Witte, A-K, Zarnekow, R. Transforming personal healthcare through technology – a systematic literature review of wearable sensors for medical application. In: Hawaii international conference on system sciences. Epub ahead of print 2019. DOI: 10.24251/hicss.2019.466.
Google Scholar15. Leuenberger, K, Gonzenbach, R, Wachter, S, et al. A method to qualitatively assess arm use in stroke survivors in the home environment. Med Biol Eng Comput 2017; 55: 141–150.
Google Scholar | Crossref | Medline16. De Lucena, DS, Stoller, O, Rowe, JB, et al. Wearable sensing for rehabilitation after stroke: bimanual jerk asymmetry encodes unique information about the variability of upper extremity recovery. IEEE Int Conf Rehabil Robot 2017; 2017: 1603–1608.
Google Scholar | Medline17. Repnik, E, Puh, U, Goljar, N, et al. Using inertial measurement units and electromyography to quantify movement during action research arm test execution. Sensors (Switzerland) 2018; 18: 2767.
Google Scholar | Crossref18. Nam, HS, Lee, WH, Seo, HG, et al. Inertial measurement unit based upper extremity motion characterization for action research arm test and activities of daily living. Sensors (Switzerland) 2019; 19: 1782.
Google Scholar | Crossref19. Valtin, M, Salchow, C, Seel, T, et al. Modular finger and hand motion capturing system based on inertial and magnetic sensors. Curr Dir Biomed Eng 2017; 3: 19–23.
Google Scholar | Crossref20. Lin, BS, Lee, IJ, Yang, SY, et al. Design of an inertial-sensor-based data glove for hand function evaluation. Sensors (Switzerland) 2018; 18: 1545.
Google Scholar | Crossref21. Franck, JA, Smeets, RJEM, Seelen, HAM. Changes in actual arm-hand use in stroke patients during and after clinical rehabilitation involving a well-defined arm-hand rehabilitation program: a prospective cohort study. PLoS One 2019; 14: e0214651.
Google Scholar | Crossref | Medline22. Chin, LF, Hayward, KS, Brauer, S. Upper limb use differs among people with varied upper limb impairment levels early post-stroke: a single-site, cross-sectional, observational study. Top Stroke Rehabil 2020; 27: 224–235.
Google Scholar | Crossref | Medline23. Chin, LF, Hayward, KS, Soh, AJA, et al. An accelerometry and observational study to quantify upper limb use after stroke during inpatient rehabilitation. Physiother Res Int 2019; 24: e1784.
Google Scholar | Crossref | Medline24. Uswatte, G, Giuliani, C, Winstein, C, et al. 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.
Google Scholar | Crossref | Medline | ISI25. Bailey, RR, Klaesner, JW, Lang, CE. An accelerometry-based methodology for assessment of real-world bilateral upper extremity activity. PLoS One 2014; 9: e103135.
Google Scholar | Crossref | Medline | ISI26. Subash, T, David, A, Skm, V, et al. Comparison of wearable sensor based algorithms for upper limb activity detection. In: International conference on neurorehabilitation (virtual format), 13–16 October 2020.
Google Scholar27. Lum, PS, Shu, L, Bochniewicz, EM, et al. Improving accelerometry-based measurement of functional use of the upper extremity after stroke: machine learning versus counts threshold method. Neurorehabil Neural Repair 2020; 34: 1078–1087.
Google Scholar | SAGE Journals | ISI28. Vega-González, A, Granat, MH. Continuous monitoring of upper-limb activity in a free-living environment. Arch Phys Med Rehabil 2005; 86: 541–548.
Google Scholar | Crossref | Medline | ISI29. Bailey, RR, Klaesner, JW, Lang, CE. Quantifying real-world upper-limb activity in nondisabled adults and adults with chronic stroke. Neurorehabil Neural Repair 2015; 29: 969–978.
Google Scholar | SAGE Journals | ISI30. Uswatte, G, Foo, WL, Olmstead, H, et al. 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.
Google Scholar | Crossref | Medline | ISI31. Tran, T, Chang, LC, Almubark, I, et al. Robust classification of functional and nonfunctional arm movement after stroke using a single wrist-worn sensor device. In: Proceedings of the 2018 IEEE International Conference on Big Data. Piscataway, NJ: IEEE, pp. 5457–5459.
Google Scholar32. Almubark, I, Chang, LC, Holley, R, et al. Machine learning approaches to predict functional upper extremity use in individuals with stroke. In: Proceedings of the 2018 IEEE International Conference on Big Data. Piscataway, NJ: IEEE, pp. 5291–5294.
Google Scholar33. Bochniewicz, EM, Emmer, G, McLeod, A, et al. Measuring functional arm movement after stroke using a single wrist-worn sensor and machine learning. J Stroke Cerebrovasc Dis 2017; 26: 2880–2887.
Google Scholar | Crossref | Medline34. Howard, IS, Ingram, JN, Körding, KP, et al. Statistics of natural movements are reflected in motor errors. J Neurophysiol 2009; 102: 1902–1910.
Google Scholar | Crossref | Medline | ISI35. Madgwick, SOH, Harrison, AJL, Vaidyanathan, R. Estimation of IMU and MARG orientation using a gradient descent algorithm. In: 2011 IEEE international conference on rehabilitation robotics (ICORR). Epub ahead of print 2011. DOI: 10.1109/ICORR.2011.5975346.
Google Scholar36. Schambra, HM, Parnandi, A, Pandit, NG, et al. Taxonomy of functional upper extremity motion. Front Neurol 2019; 10: 857.
Google Scholar | Crossref | Medline37. Uswatte, G, Qadri, LH. A behavioral observation system for quantifying arm activity in daily life after stroke. Rehabil Psychol 2009; 54: 398–403.
Google Scholar | Crossref | Medline38. Xie Q. Agree or disagree? A demonstration of an alternative statistic to Cohen?s kappa for measuring the extent and reliability of agreement between observers. In: Proceedings of the Federal Committee on Statistical Methodology Research Conference 2013 4 November.
Google Scholar39. Sterr, A, Freivogel, S, Schmalohr, D. Neurobehavioral aspects of recovery: assessment of the learned nonuse phenomenon in hemiparetic adolescents. Arch Phys Med Rehabil 2002; 83: 1726–1731.
Google Scholar | Crossref | Medline | ISI40. Trost, SG, McIver, KL, Pate, RR. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc 2005; 37: S531–S543.
Google Scholar | Crossref | Medline | ISI41. Sainburg, RL. Convergent models of handedness and brain lateralization. Front Psychol 2014; 5: 1092.
Google Scholar | Crossref | Medline42. Krizhevsky, A, Sutskever, I, Hinton, GE. ImageNet classification with deep convolutional neural networks, http://code.google.com/p/cuda-convnet/ (accessed 8 April 2021).
Google Scholar43. Graving, JM, Chae, D, Naik, H, et al. DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning. eLife 2019; 8: e.47994.
Google Scholar | Crossref | Medline44. Kantak, S, Jax, S, Wittenberg, G. Bimanual coordination: a missing piece of arm rehabilitation after stroke. Restor Neurol Neurosci 2017; 35: 347–364.
Google Scholar | Crossref | Medline45. Raghavan, P. Upper limb motor impairment after stroke. Phys Med Rehabil Clin N Am 2015; 26: 599–610.
Google Scholar | Crossref | Medline46. Taub, E, Uswatte, G, Elbert, T. New treatments in neurorehabilitation founded on basic research. Nat Rev Neurosci 2002; 3: 228–236.
Google Scholar | Crossref | Medline | ISI47. Kwakkel, G, Van Wegen, EEH, Burridge, JH, et al. Standardized measurement of quality of upper limb movement after stroke: consensus-based core recommendations from the second stroke recovery and rehabilitation roundtable. Int J Stroke 2019; 14: 783–791.
Google Scholar | SAGE Journals | ISI