RESNA Annual Conference - 2020

 Useful Information Brought From Wearable Devices To The Clinical Walk Tests In Stroke Patients

Shao-Li Han1,2, Min-Chun Pan1, Toko Sugiharto1

1National Central University, 2Cathay General Hospital (Taiwan)


Stroke has been the leading cause of long-term or permanent disabilities in adults. One of the common impairments left after stroke is weak limbs. These weak limbs or trunk can further hinder patients from walking and from returning to their functional status. The application of clinical assessment scales on stroke patients has posed a significant impact on rehabilitation. These items that patients perform poorly are the aspects that they need further training. Besides, the difference between sequential tests is a simple method to monitor patients’ training effects. Although most of these scales are easy to be administered in clinical settings, clinical experts cannot get all movement data from a single test. Along with these neuromuscular testing results, the kinematic analysis provides reliable and numeric data for clinical consultants to understand how neuromotor disorder affected stroke patients’ abilities. Today, mechanical engineers can embed many sensors into a small module, suitable for wearable devices, to gain movement data from human motion due to the advance of microelectromechanical systems. To establish reliable and accurate algorithms for estimating walking kinematics from these compact devices has been one of the major preoccupations of medical device research in the past decades [13]. Although these devices have applied to human motion analysis over the years, only recently has some researchers point how these devices can help clinical assessment scales [4].

Walking tests in stroke patients can give clinical experts many significant functional statuses and even the prognostic predictors. It is a feasible, intuitive, and reliable method in clinical units to obtain functional recovery status in post-stroke patients [59]. Over a few decades, there has been much research documenting how sarcopenia affects walking speeds. In short, walking speeds are one of the criteria to diagnose sarcopenia. However, walking speeds among stroke patients are also affected by weak limbs. It is, therefore, straightforward to conclude that stroke patients have a higher risk of getting sarcopenia [10]. Several methods have been proposed to obtain kinematics through wearable devices nowadays [11]. They are promising but challenging, such as obtaining accurate spatiotemporal data, integrating with several sensors, and other transmission issues[12]. Applying wearable devices, primarily composed of inertial measurement units in patients with neurological disorders, exist in some estimating errors in spite of their excellent performance in healthy participants [13]. One of the debating issues is integrating errors from inertial measurement units. Saremi and Marehbian note that walking speed and temporal data is less reliable in stroke with slow walking speeds (<0.5 m/s) [14].

To sum up, the walking speed test is a clinically useful tool to evaluate patients’ functional status but there still exist kinematics that has impact on walking speeds. By using IMUs, there still exists challenges. There have been several advantages and disadvantages when either adopting wearable devices alone or clinical assessment scales alone. Another attractive topic is where and how many wearables needed to obtain enough kinematics for clinical application. Integrating wearable devices with clinical assessment scales should be the intuitive and workable method to overcome those shortages. The purpose of this research focuses on setting up a wearable system and managing to integrate clinical walk tests. This hybrid method is supposed to acquire kinematics among walkable stroke patients through wearable devices and to determinate the most critical location to provide additional data during walking tests.



  1. Jiang, Gholami, Khoshnam, Eng, andMenon, “Estimation of Ankle Joint Power During Walking Using Two Inertial Sensors,” Sensors, vol. 19, no. 12, p. 2796, 2019.
  2. R.Caldas, M.Mundt, W.Potthast, F.Buarque De Lima Neto, andB.Markert, “A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms,” Gait Posture, vol. 57, pp. 204–210, 2017.
  3. D.Jarchi, J.Pope, T. K. M.Lee, L.Tamjidi, A.Mirzaei, andS.Sanei, “A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications,” IEEE Rev. Biomed. Eng., vol. 11, no. c, pp. 177–194, 2018.
  4. V.Cimolin et al., “Do wearable sensors add meaningful information to the Timed Up and Go test? A study on obese women,” J. Electromyogr. Kinesiol., vol. 44, no. November 2018, pp. 78–85, 2019.
  5. C.-L.Chen, H.-C.Chen, S. F.-T.Tang, C.-Y.Wu, P.-T.Cheng, andW.-H.Hong, “Gait performance with compensatory adaptations in stroke patients with different degrees of motor recovery.,” Am. J. Phys. Med. Rehabil., vol. 82, no. 12, pp. 925–35, Dec.2003.
  6. S.Allin, N.Baker, E.Eckel, andD.Ramanan, “Robust Tracking of the Upper Limb for Functional Stroke Assessment,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 18, no. c, pp. 1–9, 2010.
  7. C. M.Dean, C. L.Richards, andF.Malouin, “Walking speed over 10 metres overestimates locomotor capacity after stroke,” Clin. Rehabil., vol. 15, no. 4, pp. 415–421, 2001.
  8. M. K.Aaslund et al., “A longitudinal study investigating how stroke severity, disability, and physical function the first week post-stroke are associated with walking speed six months post-stroke,” Physiother Theory Pr., vol. 33, no. 12, pp. 932–942, 2017.
  9. K.Bower et al., “Dynamic balance and instrumented gait variables are independent predictors of falls following stroke,” J. Neuroeng. Rehabil., vol. 16, no. 1, pp. 1–9, 2019.
  10. A. S.Ryan, F.M.Ivery, M.C.Serra, J.Hartstein, andC. E.Hafer-Macko, “Sacropenia and Physical Function in Middle-Aged and Older Stroke Sur vivors,” Ach Phys Med Rehabil, vol. 98, no. 3, pp. 495–499, 2017.
  11. S.Bertuletti, U.DellaCroce, andA.Cereatti, “A wearable solution for accurate step detection based on the direct measurement of the inter-foot distance,” J. Biomech., vol. 84, pp. 274–277, 2019.
  12. S.Qiu, Z.Wang, H.Zhao, L.Liu, andY.Jiang, “Using Body-Worn Sensors for Preliminary Rehabilitation Assessment in Stroke Victims with Gait Impairment,” IEEE Access, vol. 6, pp. 31249–31258, 2018.
  13. D.Trojaniello, A.Ravaschio, J. M.Hausdorff, andA.Cereatti, “Comparative assessment of different methods for the estimation of gait temporal parameters using a single inertial sensor: Application to elderly, post-stroke, Parkinson’s disease and Huntington’s disease subjects,” Gait Posture, vol. 42, no. 3, pp. 310–316, 2015.
  14. K.Saremi et al., “Reliability and validity of bilateral thigh and foot accelerometry measures of walking in healthy and hemiparetic subjects,” Neurorehabil. Neural Repair, vol. 20, no. 2, p. 297(9), 2006.
  15. S. O. H.Madgwick, A. J. L.Harrison, andR.Vaidyanathan, “Estimation of IMU and MARG orientation using a gradient descent algorithm,” IEEE Int. Conf. Rehabil. Robot., 2011.
  16. Michael W. Whittle, Gait analysis an Introduction, 4th ed. Chattanooga: Heidi Harrison, 2007.
  17. P. W. H.Kwong andS. S. M.Ng, “Cutoff Score of the Lower-Extremity Motor Subscale of Fugl-Meyer Assessment in Chronic Stroke Survivors: A Cross-Sectional Study,” Arch. Phys. Med. Rehabil., vol. 100, no. 9, pp. 1782–1787, 2019.
  18. T. K.Koo andM. Y.Li, “A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research,” J. Chiropr. Med., vol. 15, no. 2, pp. 155–163, 2016.
Audio Version PDF Version