Introduction
Spinal cord injury (SCI) or cerebrovascular accident (CVA) may cause hemiplegia, paraplegia, or limb paralysis, as well as abnormal balance and gait. Patients with limb paralysis have muscle weakness, paresthesia, and joint stiffness. These patients have decreased functional ability and increased risk of falls, making it difficult for them to perform activities of daily living (ADLs) or live independently. Therefore, gait recovery is the goal of rehabilitation therapy for patients with paralysis.
Robotic-assisted gait training (RAGT), based on intensive repetitions of tasks, is widely used for improving stance and gait in paralyzed patients. Robotic-assisted gait training (RAGT) improves gait through biomechanical feedback and high-intensity repetitive walking in a real-like environment.
WALKBOT (P&S Mechanics Co., Ltd., Seoul, Korea), an exoskeleton-type robot system used for gait training, reduces the limitations of classical gait rehabilitation in paralyzed patients. WALKBOT uses the exoskeleton and a body weight support harness to induce stepping movements in accordance with a stored normal biomechanical walking pattern. The robot’s joints have axes similar to the axes of human legs; this allows natural walking movements on all leg joints, as well as exercises involving individual joint muscles. The use of WALKBOT improves gait in paralyzed patients and has the advantage of individualizing therapy for patients based on their physical characteristics.
In this study, we evaluated the effects of gait rehabilitation using WALKBOT on lower extremity strength, function, balance, and gait in patients with acute neurologic disorder.
WALKBOT (P&S Mechanics Co., Ltd., Seoul, Korea), an exoskeleton-type robot system used for gait training, reduces the limitations of classical gait rehabilitation in paralyzed patients. WALKBOT uses the exoskeleton and a body weight support harness to induce stepping movements in accordance with a stored normal biomechanical walking pattern. The robot’s joints have axes similar to the axes of human legs; this allows natural walking movements on all leg joints, as well as exercises involving individual joint muscles. The use of WALKBOT improves gait in paralyzed patients and has the advantage of individualizing therapy for patients based on their physical characteristics.
In this study, we evaluated the effects of gait rehabilitation using WALKBOT on lower extremity strength, function, balance, and gait in patients with acute neurologic disorder.
Methods
1. Participants
We administered Robotic-assisted gait training (RAGT) rehabilitation to 26 patients (SCI: 12; CVA: 14) hospitalized for hemiplegia, paraplegia, or quadriplegia at a general hospital in Wonju, Gangwon-do, South Korea(Table 1).
We used three scales for evaluation before and after training in all patients: Motricity Index (MI), Berg Balance Scale (BBS), and FAC.
3. Procedures
Patients underwent Robotic-assisted gait training (RAGT) 10–15 times over 2 weeks, up to 5 times per week, 20 min per day (total 200~300 min). For the Robotic-assisted gait training (RAGT), a WALKBOT (robot-driven aid with posture control), a weight-bearing device, and a treadmill were used. Compared with other products, the independent drive of ankle joint with WALKBOT prevents excessive plantarflexion and foot drag.
Results
1. Comparison of Clinical Outcomes
There were significant improvements in MI, FAC, and BBS scores after WALKBOT Robotic-assisted gait training (RAGT) in patients with acute incomplete SCI (p < 0.05) (Table 2) and patients with CVA (p < 0.001) (Table 3).
2. Comparison of Combined BBS Scores before and after WALKBOT RAGT
After WALKBOT Robotic-assisted gait training (RAGT) in paralyzed patients, there were significant improvements in the BBS scores for items 1–11th (p < 0.05), but not for items 12–14th (12: placing alternated foot on stool; 13: standing with one foot in front; 14: standing with one foot) (p > 0.05) (Table 4).
Discussion
Robotic-assisted gait training (RAGT) uses a task-oriented approach for paralyzed patients based on the motor re-learning theory through repetitive, high-intensity tasks. WALKBOT eliminates the therapist errors and fatigue associated with traditional physical therapy. In addition, Robotic-assisted gait training (RAGT) intensity and gait parameters (e.g., stride length, gait velocity, and gait frequency) can be adjusted with WALKBOT in accordance with the patient’s requirements. Partial support for the patient’s weight makes WALKBOT safer than traditional physical therapy. Traditional gait therapy allows 50–100 steps to be practiced each hour for wheelchair-dependent patients, whereas Robotic-assisted gait training (RAGT) allows 1000–2000 steps to be practiced in 30 min.
Conclusions
This study was aimed to investigate the effects of WALKBOT Robotic-assisted gait training (RAGT) on lower extremity strength, balance, and gait in paralyzed patients with the acute phase of incomplete SCI and CVA.
WALKBOT Robotic-assisted gait training (RAGT) had significant improvements in MI, FAC, and BBS scores in patients with acute neurologic disorders. In addition, the WALKBOT RAGT protocol used this study might be affected on improving the performance of patients on the difficult balancing tasks used in the BBS.
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