World Health Organization: Stroke. World Health Organization; 19 December 2025. https://www.who.int/news-room/fact-sheets/detail/stroke

Johnson CO, Nguyen M, Roth GA, Nichols E, Alam T, Abate D, et al. Global, regional, and national burden of stroke, 1990–2016: a systematic analysis for the global burden of disease study 2016. The Lancet Neurol. 2019;18(5):439–58.

Article  Google Scholar 

Arene N, Hidler J. Understanding motor impairment in the paretic lower limb after a stroke: a review of the literature. Top Stroke Rehabil. 2009;16(5):346–56.

Article  Google Scholar 

Balaban B, Tok F. Gait disturbances in patients with stroke. PM&R. 2014;6(7):635–42.

Article  Google Scholar 

Brock KA, Goldie PA, Greenwood KM. Evaluating the effectiveness of stroke rehabilitation: choosing a discriminative measure. Arch Phys Med Rehabil. 2002;83(1):92–9.

Article  Google Scholar 

Kim JH, Hong H, Lee K, Jeong Y, Ryu H, Kim H, et al. Ai in evaluating ambulation of stroke patients: severity classification with video and functional ambulation category scale. Top Stroke Rehabil. 2025;32(1):34–42.

Article  Google Scholar 

Stookey AD, Katzel LI, Steinbrenner G, Shaughnessy M, Ivey FM. The short physical performance battery as a predictor of functional capacity after stroke. J Stroke Cerebrovasc Dis. 2014;23(1):130–5.

Article  Google Scholar 

Itoh S, Ogino T, Kawakami K, Miyake K, Iyoda H, Imaizumi H, et al. Gait evaluation in stroke hemiplegic patients using short physical performance battery. J Stroke Cerebrovasc Dis. 2022;31(10):106704.

Article  Google Scholar 

Western MJ, Malkowski OS. Associations of the short physical performance battery (sppb) with adverse health outcomes in older adults: a 14-year follow-up from the english longitudinal study of ageing (elsa). Int J Environ Res Public Health. 2022;19(23):16319.

Article  Google Scholar 

Greene BR, Doheny EP, Kenny RA, Caulfield B. Classification of frailty and falls history using a combination of sensor-based mobility assessments. Physiol Meas. 2014;35(10):2053–66.

Article  Google Scholar 

Welch SA, Ward RE, Beauchamp MK, Leveille SG, Travison T, Bean JF. The short physical performance battery (sppb): a quick and useful tool for fall risk stratification among older primary care patients. J Am Med Dir Assoc. 2021;22(8):1646–51.

Article  Google Scholar 

Perera S, Mody SH, Woodman RC, Studenski SA. Meaningful change and responsiveness in common physical performance measures in older adults. J Am Geriatr Soc. 2006;54(5):743–9.

Article  Google Scholar 

Eusepi D, Pellicciari L, Ugolini A, Graziani L, Coppari A, Carlizza A, et al. Reliability of the short physical performance battery (sppb): a systematic review with meta-analysis. Eur Geriatric Med. 2025;16(1):1993–2008.

Article  Google Scholar 

Bai Y, Marco NR, Jia W, Zgibor J, Burke LE, Albert SM, et al. Electronic platform for automatic short physical performance battery (sppb) test. In: 2018 15th International Symposium on Pervasive Systems. Algorithms and Networks (I-SPAN). Yichang, China: IEEE; 2018. p. 311–7.

Kolakowski M, Bader SB. Ml-based short physical performance battery future score prediction based on questionnaire data. In: 2024 32nd Telecommunications Forum (TELFOR), pp. 1–4. IEEE, Belgrade, Serbia 2024.

Kim HS, Lee JW. Ai-based prediction of sppb scores using questionnaires of abilities: findings from the national health and aging trends study. BMC Med Inform Decis Mak. 2025;25(1):434.

Article  Google Scholar 

Jeon H, Chung EH, Bak SY, Kim H, Shin S, Baek H, et al. Comparison of biomechanical parameters in lower limb joints of stroke patients according to conventional evaluation scores during level walking. Front Bioeng Biotechnol. 2024;12:1320337.

Article  Google Scholar 

Panebianco GP, Bisi MC, Stagni R, Fantozzi S. Analysis of the performance of 17 algorithms from a systematic review: Influence of sensor position, analysed variable and computational approach in gait timing estimation from imu measurements. Gait & Posture. 2018;66:76–82.

Article  Google Scholar 

Lim H, Kim B, Park S. Prediction of lower limb kinetics and kinematics during walking by a single imu on the lower back using machine learning. Sensors. 2019;20(1):130.

Article  Google Scholar 

Friedrich B, Lau S, Elgert L, Bauer JM, Hein A. A deep learning approach for tug and sppb score prediction of (pre-) frail older adults on real-life imu data. Healthcare. 2021;9(2):149.

Article  Google Scholar 

Noh B, Youm C, Goh E, Lee M, Park H, Jeon H, et al. Xgboost based machine learning approach to predict the risk of fall in older adults using gait outcomes. Sci Rep. 2021;11(1):12183.

Article  Google Scholar 

Kamran F, Harrold K, Zwier J, Carender W, Bao T, Sienko KH, et al. Automatically evaluating balance using machine learning and data from a single inertial measurement unit. J Neuroeng Rehabil. 2021;18(1):114.

Article  Google Scholar 

Salarian A, Russmann H, Vingerhoets FJ, Dehollain C, Blanc Y, Burkhard PR, et al. Gait assessment in parkinson’s disease: toward an ambulatory system for long-term monitoring. IEEE Trans Biomed Eng. 2004;51(8):1434–43.

Article  Google Scholar 

Lee S, Park C, Ha E, Hong J, Kim SH, Kim Y. Determination of spatiotemporal gait parameters using a smartphone’s imu in the pocket: threshold-based and deep learning approaches. Sensors. 2025;25(14):4395.

Article  Google Scholar 

Yang S, Koo B, Lee S, Jang DJ, Shin H, Choi HJ, et al. Determination of gait events and temporal gait parameters for persons with a knee-ankle-foot orthosis. Sensors. 2024;24(3):964.

Article  Google Scholar 

Trojaniello D, Cereatti A, Pelosin E, Avanzino L, Mirelman A, Hausdorff JM, et al. Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: application to elderly, hemiparetic, parkinsonian and choreic gait. J Neuroeng Rehabil. 2014;11(1):152.

Article  Google Scholar 

Caramia C, Torricelli D, Schmid M, Muñoz-Gonzalez A, Gonzalez-Vargas J, Grandas F, et al. Imu-based classification of parkinson’s disease from gait: a sensitivity analysis on sensor location and feature selection. IEEE J Biomed Health Inform. 2018;22(6):1765–74.

Article  Google Scholar 

Wei W, Kurita K, Kuang J, Gao A. Real-time limb motion tracking with a single imu sensor for physical therapy exercises. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 7152–7157. IEEE, Virtual Conference 2021.

Seo JW, Kim SG, Kim JI, Ku B, Kim K, Lee S, et al. Principal characteristics of affected and unaffected side trunk movement and gait event parameters during hemiplegic stroke gait with imu sensor. Sensors. 2020;20(24):7338.

Article  Google Scholar 

Xu J, Witchalls J, Preston E, Pan L, Waddington G, Adams R, et al. Stroke-related factors associated with gait asymmetry in ambulatory stroke survivors: a systematic review and meta-analysis. Gait & Posture. 2025;121:173–81.

Article  Google Scholar 

Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol. 1994;49(2):85–94.

Article  Google Scholar 

Shaikh MF, Salcic Z, Wang K. Analysis and selection of the force sensitive resistors for gait characterisation. In: 2015 6th International Conference on Automation. Robotics and Applications (ICARA). Queenstown, New Zealand: IEEE; 2015. p. 370–5.

Mao Y, Ogata T, Ora H, Tanaka N, Miyake Y. Estimation of stride-by-stride spatial gait parameters using inertial measurement unit attached to the shank with inverted pendulum model. Sci Rep. 2021;11(1):1391.

Article  Google Scholar 

Sung J, Han S, Park H, Hwang S, Lee SJ, Park JW, et al. Classification of stroke severity using clinically relevant symmetric gait features based on recursive feature elimination with cross-validation. IEEE Access. 2022;10:119437–47.

Article  Google Scholar 

Gillani SI, Azam MA, Ehatisham-ul-Haq M. Age estimation and gender classification based on human gait analysis. In: 2020 International Conference on Emerging Trends in Smart Technologies (ICETST), pp. 1–6. IEEE, Karachi, Pakistan 2020.

Galasso S, Baptista R, Molinara M, Pizzocaro S, Calabrò RS, De Nunzio AM. Predicting physical activity levels from kinematic gait data using machine learning techniques. Eng Appl Artif Intell. 2023;123:106487.

Article  Google Scholar 

Hwang S, Kwon N, Lee D, Kim J, Yang S, Youn I, et al. A multimodal fatigue detection system using semg and imu signals with a hybrid cnn-lstm-attention model. Sensors. 2025;25(11):3309.

Article  Google Scholar 

Hwang S, Kim J, Yang S, Moon HJ, Cho KH, Youn I, et al. Machine learning based abnormal gait classification with imu considering joint impairment. Sensors. 2024;24(17):5571.

Article  Google Scholar 

Gouelle A, Mégrot F, Presedo A, Husson I, Yelnik A, Penneçot GF. The gait variability index: a new way to quantify fluctuation magnitude of spatiotemporal parameters during gait. Gait & Posture. 2013;38(3):461–5.

Article  Google Scholar 

Buzzi UH, Stergiou N, Kurz MJ, Hageman PA, Heidel J. Nonlinear dynamics indicates aging affects variability during gait. Clin Biomech. 2003;18(5):435–43.

Article  Google Scholar 

Bernabeu-Mora R, Medina-Mirapeix F, Llamazares-Herrán E, García-Guillamón G, Giménez-Giménez LM, Sánchez-Nieto JM. The short physical performance battery is a discriminative tool for identifying patients with copd at risk of disability. Int J Chron Obstruct Pulmon Dis. 2015;2619–26.

Pavasini R, Guralnik J, Brown JC, Di Bari M, Cesari M, Landi F, et al. Short physical performance battery and all-cause mortality: systematic review and meta-analysis. BMC Med. 2016;14(1):215.

Article  Google Scholar 

Saeys Y, Inza I, Larranaga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007;23(19):2507–17.

Article  Google Scholar 

Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res. 2003;3:1157–82.

Verma R, Arya KN, Sharma P, Garg RK. Understanding gait control in post-stroke: implications for management. J Bodyw Mov Ther. 2012;16(1):14–21.

Article  Google Scholar 

Felius RA, Geerars M, Bruijn SM, Dieën JH, Wouda NC, Punt M. Reliability of imu-based gait assessment in clinical stroke rehabilitation. Sensors. 2022;22(3):908.

Article  Google Scholar