Postural control and gait quality in individuals in the chronic phase of stroke undergoing mobile robotic-assisted training: a pilot randomized clinical trial

 Since this didn't work, ask your competent? doctor WHAT EXACT PROTOCOLS WILL RECOVER YOUR GAIT AND PROVIDE POSTURAL CONTROL! Oh darn, HAS NOTHING; So completely fucking incompetent!

And your board of directors is so incompetent they can't recognize incompetence in their hospital!

Postural control and gait quality in individuals in the chronic phase of stroke undergoing mobile robotic-assisted training: a pilot randomized clinical trial

Kamila Niewolak

¹

,

 

Joanna Antkiewicz

¹

,

 

Laura Jadwiga Piejko

^{2, 3}

,

 

Julia Duda

³

,

 

Bogna Szołtys-Brzezowska

^{3, 4}

,

 

Agnieszka Nawrat-Szołtysik

^{4, 5}

,

 

Cezary Kucio

^{4, 5}

,

 

Anna Polak

^{3, 4}

1. Medical and Rehabilitation Center, “Solanki”, Poland
2. Clinical Department of Physiotherapy in Psychiatry, Faculty of Physical Therapy, Academy of Physical Education in Katowice, Katowice, Poland
3. Department of Clinical Physiotherapy, Academy of Physical Education in Katowice, Institute of Physiotherapy and Health Sciences, Katowice, Poland
4. Institute of Physiotherapy and Health Sciences, Academy of Physical Education in Katowice, Katowice, Poland
5. Department of Physiotherapy in Internal Diseases, Academy of Physical Education in Katowice, Katowice

Research, Physiotherapy Review, 2026, 30(1), 62-81

DOI: https://doi.org/10.5114/phr.2026.160363

Online publish date: 2026/03/26

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Abstract 

Background: 

Stroke is the second leading cause of death world wide, after ischemic heart disease. The consequences of stroke can be reduced through systematic rehabilitation of patients, including various types of therapeutic exercises that mitigate neurological deficits and stimulate brain plasticity. Among the most common consequences of stroke are disturbances in body balance and gait. In stroke patients, rehabilitation robots may contribute to improvements in motor function, increased muscle strength, and enhanced gait quality. There is therefore a need for further research to determine which rehabilitation robots are most effective at different stages after stroke and for various types of motor deficits. Aims: The aim of this pilot study was to obtain preliminary evidence on whether and how exercises performed using a mobile rehabilitation robot improve body balance and gait quality in individuals in the chronic phase after stroke. 

Material and methods: 

The study included chronic post-stroke patients (n = 23) randomly divided into two groups. In the experimental group (EG), participants performed gait exercises using a mobile rehabilitation robot for 20–40 minutes once daily, 6 days per week, for 3 weeks (a total of 18 training sessions). In the control group (CG), participants performed traditional ground-based gait exercises for 10–20 minutes once daily, 6 days per week, for 3 weeks. Patients in both groups also underwent conventional post-stroke rehabilitation for 2.5 hours per day, 6 days per week, based on best clinical practice principles. 

Results: 

Eighteen training sessions performed either on a mobile robot in the EG or as ground based gait training in the CG did not significantly improve patients’ body balance or gait quality in the chronic phase after stroke. 

Conclusions: 

There is no basis to conclude that ground-based gait training has a statistically significantly greater effect on body balance than training using a mobile robot in individuals in the chronic phase after stroke. Further studies are needed to provide a clear answer as to whether and to what extent mobile robot training can im prove body balance and gait quality in individuals in the chronic phase after stroke. Future clinical trials should consider implementing training protocols consisting of more than 18 sessions

Neural mechanisms underlying the improvement of gait disturbances in stroke patients through robot-assisted gait training based on QEEG and fNIRS: a randomized controlled study

 But you DID NOTHING! No protocols written, YOU'RE FIRED!

Neural mechanisms underlying the improvement of gait disturbances in stroke patients through robot-assisted gait training based on QEEG and fNIRS: a randomized controlled study

• Xiang Li, 
• Huihuang Zhang, 
• Wanying Zhang, 
• Jianing Wu, 
• Lei Dai, 
• Nasha Long, 
• Tiefeng Jin, 
• Lei Gu & 
• Jianer Chen 

Journal of NeuroEngineering and Rehabilitation volume 22, Article number: 136 (2025) Cite this article

• 174 Accesses

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Abstract

Background

Robot-assisted gait training is more effective in improving lower limb function and walking ability in stroke patients compared to conventional rehabilitation, but the neural mechanisms remain unclear. This study aims to explore the effects of robot-assisted gait training on lower limb motor dysfunction in stroke patients and its impact on neural activity in the motor cortex, providing objective evidence for clinical application.

Methods

Forty-two stroke patients meeting the inclusion criteria were randomly assigned to either the experimental group receiving robot-assisted gait training or the control group receiving conventional overground walking training. Assessments were conducted at baseline and after four weeks of treatment. Primary outcome measures included cortical activation measured by functional near-infrared spectroscopy (fNIRS), power ratio index (PRI), and delta/alpha power ratio (DAR) measured by quantitative electroencephalography (QEEG), and their correlation with the Fugl-Meyer Assessment (FMA) for lower limb motor function. Secondary outcome measures included FMA and Functional Ambulation Category (FAC).

Results

Data from 36 patients (18 in each group) after four weeks of treatment were analyzed. The fNIRS results indicated better activation in the premotor and supplementary motor cortices in the robot-assisted gait training group compared to the control group. QEEG analysis showed reduced PRI and DAR in the premotor, supplementary motor, and primary motor cortices in the robot-assisted gait training group, suggesting improved motor function recovery in stroke patients. Clinical scale analysis revealed superior motor function recovery in the robot-assisted gait training group compared to the control group.

Conclusions

Robot-assisted gait training significantly enhances activation in the primary motor cortex and supplementary motor area, potentially aiding stroke patients in recovering their ability to plan. PRI and DAR, particularly PRI, are valuable clinical indicators for assessing motor function recovery in stroke patients.

Trial registration

Chinese Clinical Trial Registry (ChiCTR2200060668). Registered on June 6, 2022; https://www.chictr.org.cn/showproj.html?proj=171610.

Background

Stroke is a global health issue and one of the leading causes of long-term disability. Approximately one-third of stroke patients experience permanent motor deficits, severely affecting their daily activities [1]. Lower limb motor dysfunction is a common problem among stroke patients, leading to difficulties in mobility, posture maintenance, balance, and walking. Therefore, providing rehabilitation to improve walking ability in stroke patients is necessary [2, 3].

In recent years, rehabilitation robots have become increasingly important in clinical rehabilitation [4]. Their application can relieve therapists from strenuous training tasks. By analyzing data from rehabilitation robot training, the patient’s recovery status can be assessed [5]. Due to their precision and reliability, rehabilitation robots are an effective method for improving stroke rehabilitation [6].

Currently, the neurophysiological mechanisms by which rehabilitation robots enhance functional walking ability remain unclear [7, 8]. Some scholars believe that the effectiveness of rehabilitation robots in improving functional walking ability depends on the high repetition frequency and intensity of task-oriented movements [9]. Studies have shown that conventional exercise therapy can enhance patients’ neuroplasticity [10, 11]. Compared to traditional therapy, robot-assisted gait training may more effectively promote neuroplasticity mechanisms related to motor learning and functional recovery, such as sensorimotor plasticity, effective connectivity of the frontal-parietal cortex, and interhemispheric inhibition [12].

The rise of multimodal neuroimaging technologies has significantly impacted modern neuroscience. These methods contribute independently to understanding cognitive processing [13, 14] and improving clinical diagnosis [15]. Functional near-infrared spectroscopy (fNIRS) combined with quantitative electroencephalography (QEEG) is currently favored due to its non-invasiveness, low cost, and system flexibility [16]. fNIRS is suitable for monitoring cortical activation during dynamic movement, making it possible to visualize cortical activation during dynamic movement [17]. Based on this, this research will use fNIRS to detect patients before and after robot-assisted gait training, indirectly assessing cortical neural activation by observing changes in beta values across different brain regions.

QEEG can record synchronous postsynaptic potentials of cortical neurons from the scalp [18]. The raw electroencephalography (EEG) signal is amplified, digitized, mapped, and filtered to isolate narrow frequency bands (in Hz) reflecting specific brain sources and functions, typically divided into delta (0.3–3.5 Hz), theta (4–7.5 Hz), alpha (8–13 Hz), and beta (14–30 Hz) bands. This study will use the delta/alpha ratio (DAR) and the power ratio index (PRI), which is (delta + theta)/(alpha + beta), to assess the degree of motor dysfunction and motor gain in stroke patients.

Robot-assisted gait training has been shown to effectively improve walking ability, correct abnormal gait, and promote motor function recovery and balance in hemiplegic stroke patients, but its neural mechanisms remain unclear. In this study, hemiplegic stroke patients will undergo fNIRS and QEEG assessments over a four-week period both before and after receiving robot-assisted gait training and conventional gait training, with subsequent analysis of the correlations between EEG indices and clinical outcome measures. For the first time, this research combine fNIRS and QEEG to evaluate the dynamic effects of lower limb robotic rehabilitation on the motor cortex, providing multidimensional evidence to elucidate the neuroplastic mechanisms underlying lower limb robotic therapy in stroke patients and laying a theoretical foundation for the design of personalized rehabilitation protocols.

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Gait analysis system for assessing abnormal patterns in individuals with hemiparetic stroke during robot-assisted gait training: a criterion-related validity study in healthy adults

 Assessments do nothing unless they are directly followed by EXACT PROTOCOLS THAT DELIVER RECOVERY! I'd have you all fired!

Gait analysis system for assessing abnormal patterns in individuals with hemiparetic stroke during robot-assisted gait training: a criterion-related validity study in healthy adults

Issei Nakashima^1,2Daisuke Imoto³Satoshi Hirano^1*Hitoshi Konosu^2,4Yohei Otaka¹

• ¹Department of Rehabilitation Medicine, School of Medicine, Fujita Health University, Aichi, Japan
• ²Toyota Motor Corporation, Aichi, Japan
• ³Department of Rehabilitation, Fujita Health University Hospital, Aichi, Japan
• ⁴Department of Rehabilitation Medicine, Graduate School of Medicine, Fujita Health University, Aichi, Japan

Introduction: Gait robots have the potential to analyze gait characteristics during gait training using mounted sensors in addition to robotic assistance of the individual’s movements. However, no systems have been proposed to analyze gait performance during robot-assisted gait training. Our newly developed gait robot,” Welwalk WW-2000 (WW-2000)” is equipped with a gait analysis system to analyze abnormal gait patterns during robot-assisted gait training. We previously investigated the validity of the index values for the nine abnormal gait patterns. Here, we proposed new index values for four abnormal gait patterns, which are anterior trunk tilt, excessive trunk shifts over the affected side, excessive knee joint flexion, and swing difficulty; we investigated the criterion validity of the WW-2000 gait analysis system in healthy adults for these new index values.

Methods: Twelve healthy participants simulated four abnormal gait patterns manifested in individuals with hemiparetic stroke while wearing the robot. Each participant was instructed to perform 16 gait trials, with four grades of severity for each of the four abnormal gait patterns. Twenty strides were recorded for each gait trial using a gait analysis system in the WW-2000 and video cameras. Abnormal gait patterns were assessed using the two parameters: the index values calculated for each stride from the WW-2000 gait analysis system, and assessor’s severity scores for each stride. The correlation of the index values between the two methods was evaluated using the Spearman rank correlation coefficient for each gait pattern in each participant.

Results: The median (minimum to maximum) values of Spearman rank correlation coefficient among the 12 participants between the index value calculated using the WW-2000 gait analysis system and the assessor’s severity scores for anterior trunk tilt, excessive trunk shifts over the affected side, excessive knee joint flexion, and swing difficulty were 0.892 (0.749–0.969), 0.859 (0.439–0.923), 0.920 (0.738–0.969), and 0.681 (0.391–0.889), respectively.

Discussion: The WW-2000 gait analysis system captured four new abnormal gait patterns observed in individuals with hemiparetic stroke with high validity, in addition to nine previously validated abnormal gait patterns. Assessing abnormal gait patterns is important(You're completely and totally wrong here!) as improving them contributes to stroke rehabilitation.

Clinical trial registration: https://jrct.niph.go.jp, identifier jRCT 042190109.

1 Introduction

Improving gait of individuals with hemiparetic stroke through gait training is the primary goal for stroke rehabilitation (Jette et al., 2005; Latham et al., 2005). Robot-assisted gait training (RAGT) has been proposed as a method for improving the gait of individuals with hemiparetic stroke (Mehrholz et al., 2020), as it can provide intensive, repetitive, task-oriented training for those who are unable to walk independently by partially or fully supporting their weight and movements with a robot control mechanism (Morone et al., 2017). According to systematic reviews, RAGT in individuals with hemiparetic stroke is particularly effective in achieving walking independence (Mehrholz et al., 2020), and its use is recommended in treatment guidelines (Calabrò et al., 2021).

Appropriate assessment of gait characteristics in individuals with hemiparetic stroke is useful for planning treatment goals (Mulroy et al., 2003), monitoring treatment effects (Toro et al., 2003), and predicting the degree of improvement (Kaczmarczyk et al., 2012). Therefore, an important component for maximizing the effectiveness of RAGT is the appropriate assessment of the individual’s gait characteristics during gait training. Observational gait analysis is generally used in clinical practice to analyze gait characteristics (Perry et al., 2010). In contrast, the gait analysis method using a three-dimensional (3D) gait analysis device can acquire objective information, such as temporal and spatial parameters (Baker, 2006). Gait robots have the potential to contribute to quantitatively analyze gait characteristics using mounted sensors; however, no systems have been proposed to analyze gait performance during RAGT.

We developed a new gait training robot with a markerless motion capture system, Welwalk WW-2000 (WW-2000, Toyota Motor Corporation, Aichi, Japan), which enables measurement of various parameters, including time and mechanical assistance load, of an individual’s paralyzed leg. This gait robot system analyzes abnormal hemiparetic gait patterns during RAGT using various sensors (Nakashima et al., 2020). We previously proposed the following index values of nine abnormal gait patterns: hip hiking, circumduction, retropulsion of the hip, excessive hip external rotation, excessive lateral shift of the trunk over the unaffected side, knee extensor thrust, medial whip, posterior trunk tilt, and contralateral vaulting. The index values are highly correlated with those of abnormal gait patterns analyzed using an existing marker-based 3D motion capture system, indicating criterion-related validity (Imoto et al., 2022). In addition to the nine previously reported abnormal gait patterns, the WW-2000 gait analysis system can analyze index values for four more abnormal gait patterns frequently observed in individuals with hemiparetic stroke: anterior trunk tilt (Olney and Richards, 1996), excessive trunk shifts over the affected side (Carr and Shepherd, 1987), excessive knee joint flexion (de Quervain et al., 1996), and swing difficulty (Burpee and Lewek, 2015). There are no reports of objective index values using an existing marker-based 3D motion capture system for these abnormal gait patterns. However, these patterns may lead to unstable walking, decreased walking speed, and a reduced walking endurance in individuals with hemiparetic stroke. Therefore, we needed to examine the validity of the index values analyzed by the WW-2000 gait analysis system using a different research design than that of a previous study (Imoto et al., 2022). The validity of the index values for abnormal gait patterns calculated by the WW-2000 gait analysis system will be comprehensively clarified through the previous study and present study. Consequently, we expect that this gait analysis system will enable the comprehensive assessment for abnormal gait patterns that occur in individuals with hemiparetic stroke during RAGT with quantitative indicators, thereby contributing to the improvement of abnormal gait patterns.

Overall, this study aimed to propose new index values for the four abnormal gait patterns that occur during RAGT, and to clarify the criterion-related validity of the index values of the four new abnormal gait patterns calculated using the WW-2000 gait analysis system: anterior trunk tilt, excessive trunk shifts over the affected side, excessive knee joint flexion, and swing difficulty, in addition to the nine previously reported abnormal gait patterns.

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