Translation of robot-assisted rehabilitation to clinical service: a comparison of the rehabilitation effectiveness of EMG-driven robot hand assisted upper limb training in practical clinical service and in clinical trial with laboratory configuration for chronic stroke

Abstract

BackgroundRehabilitation robots can provide intensive physical training after stroke. However, variations of the rehabilitation effects in translation from well-controlled research studies to clinical services have not been well evaluated yet. This study aims to compare the rehabilitation effects of the upper limb training by an electromyography (EMG)-driven robotic hand achieved in a well-controlled research environment and in a practical clinical service.MethodsIt was a non-randomized controlled trial, and thirty-two participants with chronic stroke were recruited either in the clinical service (n = 16, clinic group), or in the research setting (n = 16, lab group). Each participant received 20-session EMG-driven robotic hand assisted upper limb training. The training frequency (4 sessions/week) and the pace in a session were fixed for the lab group, while they were flexible (1–3 sessions/week) and adaptive for the clinic group. The training effects were evaluated before and after the treatment with clinical scores of the Fugl-Meyer Assessment (FMA), Action Research Arm Test (ARAT), Functional Independence Measure (FIM), and Modified Ashworth Scale (MAS).ResultsSignificant improvements in the FMA full score, shoulder/elbow and wrist/hand (P < 0.001), ARAT (P < 0.001), and MAS elbow (P < 0.05) were observed after the training for both groups. Significant improvements in the FIM (P < 0.05), MAS wrist (P < 0.001) and MAS hand (P < 0.05) were only obtained after the training in the clinic group. Compared with the lab group, higher FIM improvement in the clinic group was observed (P < 0.05).ConclusionsThe functional improvements after the robotic hand training in the clinical service were comparable to the effectiveness achieved in the research setting, through flexible training schedules even with a lower training frequency every week. Higher independence in the daily living and a more effective release in muscle tones were achieved in the clinic group than the lab group.

Clinical Trial of HEXORR II for Robotic Hand Movement Therapy After Stroke

“…A recent study with Amadeo reported gains in the 9-hole Peg test, when subjects received the robotic training after a 3 hour session of physiotherapy that included 45 min of occupational therapy and 45 min of biomechanical training of upper and lower limbs. 34 Several other studies have used wearable hand robots (X-Glove 31 , VAEDA 32 , Hand-of-Hope 35,36 , HandSOME 59 ) that enabled practice of reach, grasp and release tasks with robotic assistance to hand movement. All of these studies reporting signi cant gains on a variety of functional scales.…”

“…Clinical trials with this robot have shown signi cant impairment reductions and functional gains in chronic stroke subjects. 35,36 The FINGER robot provides assistance to the index and middle ngers as the subject plays a video game that simulates playing a guitar. A clinical trial reported signi cant gains in several clinical scales, with authors noting that subjects with impaired proprioception bene ted less from the training.…”

Clinical Test of a Wearable, High DOF, Spring Powered Hand Exoskeleton (HandSOME II)

“…Powered hand exoskeletons that can be integrated into ADL practice have shown success in improving hand function of stroke patients [11][12][13][14]. However the electronics, motors or pneumatic power sources of these devices result in increased weight, complexity and cost.…”

Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography

“…It has been shown in different studies that different movement types can be classified from EMG activity from the muscles in the affected limb. These movements include finger movements [14], various functional hand movements, such as open/close [15,[19][20][21][22] and grasps [23], wrist extension [16], elbow and shoulder movements [24], and reaching [25]. Some of the techniques that have been used for decoding the attempted movements from the EMG are amplitude thresholds of the EMG signal envelope and proportional control [14,20], and pattern recognition approaches using, e.g., Hudgins time-domain features [15], autoregressive coefficients [22], empirical mode decomposition [26], and wavelets [27].…”