Development of a New Ankle Joint Hybrid Assistive Limb

Biomimetic Peripheral Nerve Stimulation Promotes the Rat Hindlimb Motion Modulation in Stepping: An Experimental Analysis

Peripheral nerve stimulation is an effective neuromodulation method in patients with lower extremity movement disorders caused by stroke, spinal cord injury, or other diseases. However, most current studies on rehabilitation using sciatic nerve stimulation focus solely on ankle motor regulation through stimulation of common peroneal and tibial nerves. Using the electrical nerve stimulation method, we here achieved muscle control via different sciatic nerve branches to facilitate the regulation of lower limb movements during stepping and standing. A map of relationships between muscles and nerve segments was established to artificially activate specific nerve fibers with the biomimetic stimulation waveform. Then, characteristic curves depicting the relationship between neural electrical stimulation intensity and joint control were established. Finally, by testing the selected stimulation parameters in anesthetized rats, we confirmed that single-cathode extraneural electrical stimulation could activate combined movements to promote lower limb movements. Thus, this method is effective and reliable for use in treatment for improving and rehabilitating lower limb motor dysfunction.

Combined Ankle Robot Training and Robot-assisted Gait Training Improved the Gait Pattern of a Patient with Chronic Traumatic Brain Injury

Background

: Walking disability caused by central nervous system injury often lingers. In the chronic phase, there is great need to improve walking speed and gait, even for patients who walk independently. Robot-assisted gait training (RAGT) has been widely used, but few studies have focused on improving gait patterns, and its effectiveness for motor function has been limited. This report describes the combination of "RAGT to learn the gait pattern" and "ankle robot training to improve motor function" in a patient with chronic stage brain injury.

Case

: A 34-year-old woman suffered a traumatic brain injury 5 years ago. She had residual right hemiplegia [Fugl-Meyer Assessment-Lower Extremity (FMA-LE): 18 points] and mild sensory impairment, but she walked independently with a short leg brace and a cane. Her comfortable gait speed was 0.57 m/s without an orthosis, and her 6-m walk test distance was 240 m. The Gait Assessment and Intervention Tool (G.A.I.T.) score was 35 points. After hospitalization, ankle robot training was performed daily, with RAGT performed 10 times in total. Post-intervention evaluation performed on Day 28 showed: FMA-LE, 23 points; comfortable walking speed, 0.69 m/s; G.A.I.T., 27 points; and three-dimensional motion analysis showed ankle dorsiflexion improved from 3.22° to 12.59° and knee flexion improved from 1.75° to 16.54° in the swing phase.

Discussion

: This is one of few studies to have examined the combination of two robots. Combining the features of each robot improved the gait pattern and motor function, even in the chronic phase.

New Neuromuscular Training for Peripheral Nerve Disorders Using an Ankle Joint Hybrid Assistive Limb: A Case Series

Peripheral nerve disorder of the lower extremities causes drop foot and disturbs the daily living activities of patients. The ankle joint hybrid assistive limb (HAL) provides voluntary ankle joint training using surface bioelectrical signals from the muscles of the lower extremities. We investigated the neurological effects of ankle joint HAL training in three patients. Sensory nerve action potentials (SNAPs) and compound muscle action potentials (CMAPs) were analyzed for the peroneal and tibial nerves prior to the first ankle joint HAL training session. Integrated surface electromyography EMG signals were recorded before and after the HAL training sessions to evaluate the effects of training for neuromuscular disorders. The patients were hospitalized to receive rehabilitation with HAL training for 2 weeks. The HAL training was performed daily with two 60 min sessions. All cases demonstrated severe neuromuscular impairment according to the result of the CMAP. All integrated EMG measurements of antagonistic muscle activities decreased after the ankle joint HAL training. The manual muscle testing (MMT) scores of each muscle were slightly increased after the HAL intervention for Case 2(tibialis anterior, from 2 to 2+; gastrocnemius muscles, from 2- to 2; extensor digitorum longus, and extensor hallucis longus, from 1 to 3). The MMT scores were also slightly increased except for gastrocnemius muscle for Case 3 (tibialis anterior, extensor digitorum longus, and extensor hallucis longus, from 2- to 2). These two patients demonstrated voluntary muscle contractions and nerve signals in the CMAP before the HAL training. Even though the amplitude of CMAPs was low, the HAL training may provide voluntary ankle joint movements by reducing the antagonistic muscle contraction via computer processing. The HAL training may enhance muscle movement and coordination through motor learning feedback.

Robotic Technology in Foot and Ankle Surgery: A Comprehensive Review

Recent developments in robotic technologies in the field of orthopaedic surgery have largely been focused on higher volume arthroplasty procedures, with a paucity of attention paid to robotic potential for foot and ankle surgery. The aim of this paper is to summarize past and present developments foot and ankle robotics and describe outcomes associated with these interventions, with specific emphasis on the following topics: translational and preclinical utilization of robotics, deep learning and artificial intelligence modeling in foot and ankle, current applications for robotics in foot and ankle surgery, and therapeutic and orthotic-related utilizations of robotics related to the foot and ankle. Herein, we describe numerous recent robotic advancements across foot and ankle surgery, geared towards optimizing intra-operative performance, improving detection of foot and ankle pathology, understanding ankle kinematics, and rehabilitating post-surgically. Future research should work to incorporate robotics specifically into surgical procedures as other specialties within orthopaedics have done, and to further individualize machinery to patients, with the ultimate goal to improve perioperative and post-operative outcomes.

Using Deep Learning to Predict Minimum Foot-Ground Clearance Event from Toe-Off Kinematics

Efficient, adaptive, locomotor function is critically important for maintaining our health and independence, but falls-related injuries when walking are a significant risk factor, particularly for more vulnerable populations such as older people and post-stroke individuals. Tripping is the leading cause of falls, and the swing-phase event Minimum Foot Clearance (MFC) is recognised as the key biomechanical determinant of tripping probability. MFC is defined as the minimum swing foot clearance, which is seen approximately mid-swing, and it is routinely measured in gait biomechanics laboratories using precise, high-speed, camera-based 3D motion capture systems. For practical intervention strategies designed to predict, and possibly assist, swing foot trajectory to prevent tripping, identification of the MFC event is essential; however, no technique is currently available to determine MFC timing in real-life settings outside the laboratory. One strategy has been to use wearable sensors, such as Inertial Measurement Units (IMUs), but these data are limited to primarily providing only tri-axial linear acceleration and angular velocity. The aim of this study was to develop Machine Learning (ML) algorithms to predict MFC timing based on the preceding toe-off gait event. The ML algorithms were trained using 13 young adults' foot trajectory data recorded from an Optotrak 3D motion capture system. A Deep Learning configuration was developed based on a Recurrent Neural Network with a Long Short-Term Memory (LSTM) architecture and Huber loss-functions to minimise MFC-timing prediction error. We succeeded in predicting MFC timing from toe-off characteristics with a mean absolute error of 0.07 s. Although further algorithm training using population-specific inputs are needed. The ML algorithms designed here can be used for real-time actuation of wearable active devices to increase foot clearance at critical MFC and reduce devastating tripping falls. Further developments in ML-guided actuation for active exoskeletons could prove highly effective in developing technologies to reduce tripping-related falls across a range of gait impaired populations.

Robot-Assisted Ankle Rehabilitation Using the Hybrid Assistive Limb for Children after Equinus Surgery: A Report of Two Cases

After equinus corrective surgery, repetitive exercises for ankle dorsiflexion and plantar flexion are crucial during rehabilitation. The single-joint Hybrid Assistive Limb (HAL-SJ) is an advanced exoskeletal robotic device with a control system that uses bioelectrical signals to assist joint motion in real time and demonstrates joint torque assistance with the wearer's voluntary movement. We present two cases of robot-assisted ankle rehabilitation after equinus surgery using the HAL-SJ in children. Case 1 was an 8-year-old boy, whereas case 2 was a 6-year-old boy. When they were allowed to walk without braces, training with the HAL-SJ was performed postoperatively for 20 min per session a total of eight times (2-4 sessions per week). Assessments were performed before and after HAL-SJ training. During gait analysis, case 1 had improved joint angles during the stance phase on the operated side; however, case 2 had improved joint angles during the stance and swing phases. The co-activation index values of the medial gastrocnemius and tibialis anterior muscles, which were high before training, decreased after training and approached the standard value. The HAL-SJ may provide systematic feedback regarding voluntary ankle dorsiflexion and plantar flexion and is considered to have motor learning effects.

Gait Biomechanics for Fall Prevention among Older Adults

In our currently ageing society, fall prevention is important for better healthy life expectancy and sustainable healthcare systems. While active outdoor walking is recommended as adequate exercise for the senior population, falls due to tripping and slipping exist as the primary causes of severe injuries. Minimum foot clearance (MFC) is the lowest vertical height of the foot during the mid-swing phase and indicates the risk of tripping. In contrast, coefficient of friction (COF) factors determine the occurrence of falls from slipping. Optimisation of the MFC and the COF for every step cycle prevents tripping and slipping, respectively. Even after the initiation of hazardous balance loss (i.e., tripping and slipping), falls can still be prevented as long as the requirements for balance are restored. Biomechanically, dynamic balance is defined by the bodily centre of mass and by the base of support: spatially—margin of stability and temporally—available response time. Fall prevention strategies should, therefore, target controlling the MFC, the COF and dynamic balance. Practical intervention strategies include footwear modification (i.e., shoe-insole geometry and slip-resistant outsoles), exercise (i.e., ankle dorsiflexors and core stabilisers) and technological rehabilitation (i.e., electrical stimulators and active exoskeletons). Biomechanical concepts can be practically applied to various everyday settings for fall prevention among the older population.