Peripheral neuropathy in Parkinson's disease: prevalence and functional impact on gait and balance.

Peripheral neuropathy is a common problem in patients with Parkinson's disease. Peripheral neuropathy's prevalence in Parkinson's disease varies between 4.8-55%, compared with 9% in the general population. It remains unclear whether peripheral neuropathy leads to decreased motor performance in Parkinson's disease, resulting in impaired mobility and increased balance deficits. We aimed to determine the prevalence and type of peripheral neuropathy in Parkinson's disease patients and evaluate its functional impact on gait and balance. A cohort of consecutive Parkinson's disease patients assessed by movement disorders specialists based on the UK Brain Bank criteria underwent clinical, neurophysiological (nerve conduction studies and quantitative sensory testing) and neuropathological (intraepidermal nerve fibre density in skin biopsy punches) evaluation to characterize the peripheral neuropathy type and aetiology using a cross-sectional design. Gait and balance were characterized using wearable health-technology in OFF and ON medication states, and the main parameters were extracted using validated algorithms. A total of 99 Parkinson's disease participants with a mean age of 67.2 (±10) years and mean disease duration of 6.5 (±5) years were assessed. Based on a comprehensive clinical, neurophysiological and neuropathological evaluation, we found that 40.4% of Parkinson's disease patients presented peripheral neuropathy, with a predominance of small fibre neuropathy (70% of the group). In the OFF state, the presence of peripheral neuropathy was significantly associated with shorter stride length (P = 0.029), slower gait speed (P = 0.005) and smaller toe-off angles (P = 0.002) during straight walking; significantly slower speed (P = 0.019) and smaller toe-off angles (P = 0.007) were also observed during circular walking. In the ON state, the above effects remained, albeit moderately reduced. With regard to balance, significant differences between Parkinson's disease patients with and without peripheral neuropathy were observed in the OFF medication state during stance with closed eyes on a foam surface. In the ON states, these differences were no longer observable. We showed that peripheral neuropathy is common in Parkinson's disease and influences gait and balance parameters, as measured with mobile health-technology. Our study supports that peripheral neuropathy recognition and directed treatment should be pursued in order to improve gait in Parkinson's disease patients and minimize balance-related disability, targeting individualized medical care.

Gait Event Detection for Stroke Patients during Robot-Assisted Gait Training.

Functional electrical stimulation and robot-assisted gait training are techniques which are used in a clinical routine to enhance the rehabilitation process of stroke patients. By combining these technologies, therapy effects could be further improved and the rehabilitation process can be supported. In order to combine these technologies, a novel algorithm was developed, which aims to extract gait events based on movement data recorded with inertial measurement units. In perspective, the extracted gait events can be used to trigger functional electrical stimulation during robot-assisted gait training. This approach offers the possibility of equipping a broad range of potential robot-assisted gait trainers with functional electrical stimulation. In particular, the aim of this study was to test the robustness of the previously developed algorithm in a clinical setting with patients who suffered a stroke. A total amount of N = 10 stroke patients participated in the study, with written consent. The patients were assigned to two different robot-assisted gait trainers (Lyra and Lokomat) according to their performance level, resulting in five recording sessions for each gait-trainer. A previously developed algorithm was applied and further optimized in order to extract the gait events. A mean detection rate across all patients of 95.8% ± 7.5% for the Lyra and 98.7% ± 2.6% for the Lokomat was achieved. The mean type 1 error across all patients was 1.0% ± 2.0% for the Lyra and 0.9% ± 2.3% for the Lokomat. As a result, the developed algorithm was robust against patient specific movements, and provided promising results for the further development of a technique that can detect gait events during robot-assisted gait training, with the future aim to trigger functional electrical stimulation.

Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training.

Technologies such as robot-assisted gait trainers or functional electrical stimulation can improve the rehabilitation process of people affected with gait disorders due to stroke or other neurological defects. By combining both technologies, the potential disadvantages of each technology could be compensated and simultaneously, therapy effects could be improved. Thus, an algorithm was designed that aims to detect the gait cycle of a robot-assisted gait trainer. Based on movement data recorded with inertial measurement units, gait events can be detected. These events can further be used to trigger functional electrical stimulation. This novel setup offers the possibility of equipping a broad range of potential robot-assisted gait trainers with functional electrical stimulation. The aim of this paper in particular was to test the feasibility of a system using inertial measurement units for gait event detection during robot-assisted gait training. Thus, a 39-year-old healthy male adult executed a total of six training sessions with two robot-assisted gait trainers (Lokomat and Lyra). The measured data from the sensors were analyzed by a custom-made gait event detection algorithm. An overall detection rate of 98.1% ± 5.2% for the Lokomat and 94.1% ± 6.8% for the Lyra was achieved. The mean type-1 error was 0.3% ± 1.2% for the Lokomat and 1.9% ± 4.3% for the Lyra. As a result, the setup provides promising results for further research and a technique that can enhance robot-assisted gait trainers by adding functional electrical stimulation to the rehabilitation process.