Tesi etd-03082022-123042
Link copiato negli appunti
Tipo di tesi
Dottorato
Autore
BOWMAN, THOMAS
URN
etd-03082022-123042
Titolo
Robotic and wearable devices for balance and gait rehabilitation in neurological diseases
Settore scientifico disciplinare
ING-IND/34
Corso di studi
Istituto di Biorobotica - BIOROBOTICS
Commissione
relatore Prof.ssa CARROZZA, MARIA CHIARA
Parole chiave
- balance rehabilitation
- end-effector
- exoskeleton
- gait rehabilitation
- neurological diseases
- vibrotactile biofeedback
- wearable system
Data inizio appello
16/05/2022;
Disponibilità
parziale
Riassunto analitico
In the last years the application of robotic and wearable devices for balance and gait rehabilitation have shown a great potential in neurological diseases allowing clinicians an unprecedented ability to monitor and control movement components. Despite the increasing amount of randomized controlled trials on the topic, several questions need to be answered to clarify the role of these devices in the healthcare system and to facilitate their adoption in everyday clinical practice. Furthermore, new wearable devices should be designed and validated to measure gait features of specific neurological diseases. As walking impairments are a major concern in neurological rehabilitation, these devices should be integrated with biofeedback rehabilitative strategies according to the therapeutic needs of the target population. Finally, their effectiveness in improving gait pattern should be tested in a clinical setting.
As previously stated, this thesis reviewed existing literature to provide the most up to date knowledge about robotic and wearable devices for balance and gait rehabilitation in the neurological population. Moreover, this research focused on the validation and the clinical application of a wearable device to assess gait features and provide corrective biofeedback to improve walking in people with Parkinson’s disease.
Systematic reviews conducted for this research project showed that robot assisted gait training seems more effective than unspecific conventional physical therapy while it shows similar effects when compared to specific balance and gait training. Indeed, results highlighted that robotic rehabilitation provides the therapy required to reach clinically meaningful improvements in people with Multiple Sclerosis and Parkinson disease. Similarly, our review on current randomized controlled trials about wearable biofeedback devices revealed their positive effect on dynamic balance and gait for people with neurological diseases. Their abilities to measure body parameters during real-time walking and provide biofeedback signals to improve movement execution makes them suitable for the rehabilitation of functional tasks in real-life conditions.
On the other hand, the proposed reviews revealed that there is no connection between the type of robotic device, protocols used and disease severity. Wearable biofeedback devices integrating pressure and electromyographic sensors were mainly used to improve weight-bearing and muscle recruitment on the paretic leg in stroke patients, while inertial and pressure sensors were used to control axial segments, limbs, and gait features in people with Parkinson’s disease.
This thesis subsequently investigated the effectiveness of a wearable device that provides vibrotactile biofeedback in improving gait patterns in people with Parkinson disease during overground walking. The device was a novel portable system that was created to provide non-invasive, bilateral stimulations at waist level by means of vibrating motors synchronously with gait events detected by a pair of shoes with pressure-sensitive insoles.
A preliminary study verified the capability of the device to detect temporal gait events in people with Parkinson disease and compared them with the gold standard measurement system. Then, two vibrotactile biofeedback strategies were designed and applied in an experimental protocol to improve the gait pattern of participants. Results showed a reliable temporal gait events detection in Parkinsonian patients with mild to moderate disability. Furthermore, a short training period with concurrent vibrotactile biofeedback was able to increase the distance walked during functional tests and improved gait parameters measured with instrumental analyses. Future experiments are required to investigate motor learning determinants and to identify the optimal training modalities for this type of device.
In conclusion, this research collected most up to date literature on exoskeletons, end-effectors, and wearable biofeedback devices highlighting their potential advantages as enabling technologies for balance and gait training for the neurological population. Moreover, this work set out to contribute to advancing the knowledge of wearable devices and biofeedback strategies to improve walking patterns in people with Parkinson disease.
As previously stated, this thesis reviewed existing literature to provide the most up to date knowledge about robotic and wearable devices for balance and gait rehabilitation in the neurological population. Moreover, this research focused on the validation and the clinical application of a wearable device to assess gait features and provide corrective biofeedback to improve walking in people with Parkinson’s disease.
Systematic reviews conducted for this research project showed that robot assisted gait training seems more effective than unspecific conventional physical therapy while it shows similar effects when compared to specific balance and gait training. Indeed, results highlighted that robotic rehabilitation provides the therapy required to reach clinically meaningful improvements in people with Multiple Sclerosis and Parkinson disease. Similarly, our review on current randomized controlled trials about wearable biofeedback devices revealed their positive effect on dynamic balance and gait for people with neurological diseases. Their abilities to measure body parameters during real-time walking and provide biofeedback signals to improve movement execution makes them suitable for the rehabilitation of functional tasks in real-life conditions.
On the other hand, the proposed reviews revealed that there is no connection between the type of robotic device, protocols used and disease severity. Wearable biofeedback devices integrating pressure and electromyographic sensors were mainly used to improve weight-bearing and muscle recruitment on the paretic leg in stroke patients, while inertial and pressure sensors were used to control axial segments, limbs, and gait features in people with Parkinson’s disease.
This thesis subsequently investigated the effectiveness of a wearable device that provides vibrotactile biofeedback in improving gait patterns in people with Parkinson disease during overground walking. The device was a novel portable system that was created to provide non-invasive, bilateral stimulations at waist level by means of vibrating motors synchronously with gait events detected by a pair of shoes with pressure-sensitive insoles.
A preliminary study verified the capability of the device to detect temporal gait events in people with Parkinson disease and compared them with the gold standard measurement system. Then, two vibrotactile biofeedback strategies were designed and applied in an experimental protocol to improve the gait pattern of participants. Results showed a reliable temporal gait events detection in Parkinsonian patients with mild to moderate disability. Furthermore, a short training period with concurrent vibrotactile biofeedback was able to increase the distance walked during functional tests and improved gait parameters measured with instrumental analyses. Future experiments are required to investigate motor learning determinants and to identify the optimal training modalities for this type of device.
In conclusion, this research collected most up to date literature on exoskeletons, end-effectors, and wearable biofeedback devices highlighting their potential advantages as enabling technologies for balance and gait training for the neurological population. Moreover, this work set out to contribute to advancing the knowledge of wearable devices and biofeedback strategies to improve walking patterns in people with Parkinson disease.
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