Trainer in a pocket - proof-of-concept of mobile, real-time, foot kinematics feedback for gait pattern normalization in individuals after stroke, incomplete spinal cord injury and elderly patients

Abstract

BackgroundWalking disabilities negatively affect inclusion in society and quality of life and increase the risk for secondary complications. It has been shown that external feedback applied by therapists and/or robotic training devices enables individuals with gait abnormalities to consciously normalize their gait pattern. However, little is known about the effects of a technically-assisted over ground feedback therapy. The aim of this study was to assess whether automatic real-time feedback provided by a shoe-mounted inertial-sensor-based gait therapy system is feasible in individuals with gait impairments after incomplete spinal cord injury (iSCI), stroke and in the elderly.MethodsIn a non-controlled proof-of-concept study, feedback by tablet computer-generated verbalized instructions was given to individuals with iSCI, stroke and old age for normalization of an individually selected gait parameter (stride length, stance or swing duration, or foot-to-ground angle). The training phase consisted of 3 consecutive visits. Four weeks post training a follow-up visit was performed. Visits started with an initial gait analysis (iGA) without feedback, followed by 5 feedback training sessions of 2–3 min and a gait analysis at the end. A universal evaluation and FB scheme based on equidistant levels of deviations from the mean normal value (1 level = 1 standard deviation (SD) of the physiological reference for the feedback parameter) was used for assessment of gait quality as well as for automated adaptation of training difficulty. Overall changes in level over iGAs were detected using a Friedman’s Test. Post-hoc testing was achieved with paired Wilcoxon Tests. The users’ satisfaction was assessed by a customized questionnaire.ResultsFifteen individuals with iSCI, 11 after stroke and 15 elderly completed the training. The average level at iGA significantly decreased over the visits in all groups (Friedman’s test, p < 0.0001), with the biggest decrease between the first and second training visit (4.78 ± 2.84 to 3.02 ± 2.43, p < 0.0001, paired Wilcoxon test). Overall, users rated the system’s usability and its therapeutic effect as positive.ConclusionsMobile, real-time, verbalized feedback is feasible and results in a normalization of the feedback gait parameter. The results form a first basis for using real-time feedback in task-specific motor rehabilitation programs.Trial registrationDRKS00011853, retrospectively registered on 2017/03/23.Electronic supplementary materialThe online version of this article (10.1186/s12984-018-0389-4) contains supplementary material, which is available to authorized users.

Feasibility of a real-time pattern-based kinematic feedback system for gait retraining in pediatric cerebral palsy

“…The biofeedback gait training group improved significantly more on balance over 18 visits and maintained quality of life at 4-week follow-up, whereas the conventional gait training group deteriorated. Schliessmann et al 11 developed a gait training system that used shoe-fixed IMUs to provide verbal feedback on the foot-toground angle, stride length, stance duration, and/or swing duration depending on which parameter showed the highest deviation from the physiological norm in each participant. The deviation from a normal gait pattern significantly decreased over 3 visits in three groups of subjects in populations with or at risk for gait impairments: individuals with incomplete spinal cord injury, individuals post-stroke, and individuals aged 65 or older.…”

Biofeedback for Post-stroke Gait Retraining: A Review of Current Evidence and Future Research Directions in the Context of Emerging Technologies

“…The accessibility and ease-of-use of new wearable sensor-based gait biofeedback systems may make biofeedback gait training more readily available across diverse practice settings ( 69 , 70 ). Potentially, a single clinical facility could have a “biofeedback toolbox” with multiple options from which to choose, allowing clinicians to tailor the feedback mode, target, and method that best matches each patient's baseline clinical profile.…”

“…Potentially, a single clinical facility could have a “biofeedback toolbox” with multiple options from which to choose, allowing clinicians to tailor the feedback mode, target, and method that best matches each patient's baseline clinical profile. Wearable sensors can also be compatible with use at home and in community settings ( 70 ), enabling high-quality stepping practice outside of the clinic, using customized gait target parameters prescribed by the clinician during in-clinic or tele-rehabilitation evaluations. Considering the established role of repetitive stepping practice in promoting motor recovery, gait biofeedback could become a pivotal addition to a home exercise program for post-stroke individuals ( 71 ).…”

The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review

“…Therefore, in Table 1, we only report the ranges of acquisition times that go from hours to years. Among the selected studies, as displayed in Figure 3 [40,43,45,48,49,[51][52][53]62,64,[67][68][69]87,91,95,99,102], 8% (N = 6) focused on aging and associated pathologies [38,56,66,88,91,100], and 4% (N = 3) focused on diseases associated with poor lifestyle [42,62,74]. Finally, five studies were classified as "others" [41,46,82,84,93] because they could not be grouped together in an existing group.…”

“…[35][36][37][38][39]41,44,46,47,50,51,53,54,56,58,59,64,66,71,[75][76][77][78][79]81,[83][84][85][86][90][91][92][93][94]96,97,[101][102][103], others use annotations made by experts on data from videos or measurements during the experiment[37,38,40,43,48,52,55,63,67,70,74,80,…”