Patient-Specific Robot-Assisted Stroke Rehabilitation Guided by EEG - A Feasibility Study.

Multi-session robot-assisted stroke rehabilitation program requires patients to perform repetitive tasks. It is challenging for the patient to maintain concentration during training sessions. A novel intervention strategy using Electroencephalography (EEG) signals is proposed to maintain concentration during training by enhancing the engagement of stroke patients using robot-assisted multi-session rehabilitation. The approach is illustrated by applying it to one stroke patient undergoing 12 training sessions of hand motor training on the AMADEO rehabilitation device. AMADEO offers four modes of training programs of increased intensity comprising passive training, passive training with biofeedback, assistive training as well as active 2D training games. The EEG signals are measured over eight electrode sites: FC4, C4, CP4, FC3, C3, CP3, Cz, and CPz during each training day to extract movement-related cortical potential (MRCP) signals. Moreover, functional hand recovery parameters are determined using the AMADEO assessment tool. The patient's level of engagement is determined by the negative amplitude of the MRCP signal. The rehabilitation program is switched to a more intense training mode when a consistent decrease is observed in the negative amplitude of MRCP signals from the monitored electrodes. Using this approach, the rehabilitation program becomes patient-specific and adaptive. In addition, it is shown that each training mode exhibits a different recovery level of the affected hand and maximum recovery is achieved when MRCP signals indicate that the patient is actively participating in the training.

Quantitative Frailty Assessment Based on Kinematic Parameters of Daily Living Activities.

Frailty is a prevailing phenomena in older people. It is an age related syndrome that can increase the risk of fall in elderly. The people with age above 65 suffers from various functional decline and cognitive impairments. Such deficiencies are conventionally measured subjectively by geriatrics using questionnaire-based methods and clinical tests. Activities of daily living are also assessed in clinical settings by analysing simple tasks performed by the subject such as sit to stand and walking some distances. The clinical methods used to assess frailty and analyse the activity of daily living are subjective in nature and prone to human error. An objective method is proposed to quantitatively measure frailty using inertial sensor mounted on healthy, frail and nonfrail subjects while performing the sit to stand test (SiSt). An artificial neural networks based algorithm is developed to classify the frailty by extracting a unique set of features from 2D -Centre of Mass (CoM) trajectories derived from SiSt clinical test. The results indicate that the proposed algorithms provides an objective assessment of frailty that can be used by geriatrics in turn to make a more objective judgement of frailty status of older people.

Assessment of frailty: a survey of quantitative and clinical methods.

BACKGROUND: Frailty assessment is a critical approach in assessing the health status of older people. The clinical tools deployed by geriatricians to assess frailty can be grouped into two categories; using a questionnaire-based method or analyzing the physical performance of the subject. In performance analysis, the time taken by a subject to complete a physical task such as walking over a specific distance, typically three meters, is measured. The questionnaire-based method is subjective, and the time-based performance analysis does not necessarily identify the kinematic characteristics of motion and their root causes. However, kinematic characteristics are crucial in measuring the degree of frailty. RESULTS: The studies reviewed in this paper indicate that the quantitative analysis of activity of daily living, balance and gait are significant methods for assessing frailty in older people. Kinematic parameters (such as gait speed) and sensor-derived parameters are also strong markers of frailty. Seventeen gait parameters are found to be sensitive for discriminating various frailty levels. Gait velocity is the most significant parameter. Short term monitoring of daily activities is a more significant method for frailty assessment than is long term monitoring and can be implemented easily using clinical tests such as sit to stand or stand to sit. The risk of fall can be considered an outcome of frailty. CONCLUSION: Frailty is a multi-dimensional phenomenon that is defined by various domains; physical, social, psychological and environmental. The physical domain has proven to be essential in the objective determination of the degree of frailty in older people. The deployment of inertial sensor in clinical tests is an effective method for the objective assessment of frailty.

Body postural sway analysis in older people with different fall histories.

A cross-sectional study of postural sway analysis in older non-fallers, once-fallers and multiple-fallers using five common standing tests was conducted. Eighty-six older subjects with an average age of 80.4 years (SD ± 7.9) participated in the study. The angular rotation and velocity of the trunk of the participants in the roll (lateral) and pitch (sagittal) planes were recorded using an inertial sensor mounted on their lower backs. The Gaussian Mixture Models (GMM), Expectation-Maximisation (EM) and the Minimum Message Length (MML) algorithms were applied to the acquired data to obtain an index indicative of the body sway. The standing with feet together and standing with one foot in front, sway index distinguished older fallers from non-fallers with specificity of 75.7% and 77.7%, respectively, and sensitivity of 78.6% and 82.1%, respectively. This compares favourably with the Berg Balance Scales (BBS) with specificity of 70.5% and sensitivity of 75.3%. The results suggest that the proposed method has potential as a protocol to diagnose balance disorder in older people. Graphical abstract.

The Combined Effects of Adaptive Control and Virtual Reality on Robot-Assisted Fine Hand Motion Rehabilitation in Chronic Stroke Patients: A Case Study.

Robot-assisted therapy is regarded as an effective and reliable method for the delivery of highly repetitive training that is needed to trigger neuroplasticity following a stroke. However, the lack of fully adaptive assist-as-needed control of the robotic devices and an inadequate immersive virtual environment that can promote active participation during training are obstacles hindering the achievement of better training results with fewer training sessions required. This study thus focuses on these research gaps by combining these 2 key components into a rehabilitation system, with special attention on the rehabilitation of fine hand motion skills. The effectiveness of the proposed system is tested by conducting clinical trials on a chronic stroke patient and verified through clinical evaluation methods by measuring the key kinematic features such as active range of motion (ROM), finger strength, and velocity. By comparing the pretraining and post-training results, the study demonstrates that the proposed method can further enhance the effectiveness of fine hand motion rehabilitation training by improving finger ROM, strength, and coordination.

Clinical effectiveness of combined virtual reality and robot assisted fine hand motion rehabilitation in subacute stroke patients.

Robot-assisted therapy is regarded as an effective and reliable method for the delivery of highly repetitive rehabilitation training in restoring motor skills after a stroke. This study focuses on the rehabilitation of fine hand motion skills due to their vital role in performing delicate activities of daily living (ADL) tasks. The proposed rehabilitation system combines an adaptive assist-as-needed (AAN) control algorithm and a Virtual Reality (VR) based rehabilitation gaming system (RGS). The developed system is described and its effectiveness is validated through clinical trials on a group of eight subacute stroke patients for a period of six weeks. The impact of the training is verified through standard clinical evaluation methods and measuring key kinematic parameters. A comparison of the pre- and post-training results indicates that the method proposed in this study can improve fine hand motion rehabilitation training effectiveness.