Teste e Reteste da avaliação da espasticidade e sinais mecanomiográficos de flexores e extensores de cotovelo em atleta de Bocha Paralímpica com Paralisia Cerebral

Objetivo: Avaliar a eficácia da mecanomiografia (MMG) para classificação de atletas na bocha paralímpica. Método: Neste estudo piloto, o voluntário selecionado com Paralisia Cerebral, foi avaliado empregando o teste de espasticidade (ASAS) nos braços direito e esquerdo, coletados os dados de mecanomiografia durante esse teste, por meio de dois sensores de MMG. O sensor 1 foi fixado na superfície da pele, no ponto motor dos flexores do cotovelo e o sensor 2, no ponto motor dos extensores do cotovelo. Os sinais de MMG foram processados utilizando o software MATLAB®, no qual o desvio padrão foi determinado para cada eixo de cada sensor, como também a média dos desvios entre sessões para os lados direito e esquerdo dos músculos flexores e extensores dispostos para cada avaliador. Resultados: Constataram-se diferenças numéricas entre as médias dos desvios para cada avaliador do mesmo grupo muscular do mesmo braço; porém, estas diferenças são sutis e mostram um padrão para o sinal mecanomiográficos mesmo quando diferentes avaliadores utilizam realizam o teste. Conclusão: Conclui-se que a MMG é viável na utilização de identificação espasticidade e os valores da média de todas as avaliações dos avaliadores 1 e 2 no grupo de flexores (MSD) foi mantida entre 0,1723 mV (Y) e 0,1225 mV (Z), 0,1904 (Y) mV a 0,1601mV (Z), não havendo divergência entre os avaliadores, mas caso houvesse o MMG seria fundamental na avaliação de espasticidade

Spasticity Assessment Based on the Maximum Isometrics Voluntary Contraction of Upper Limb Muscles in Post-stroke Hemiplegia

Background: The assessment of muscle properties is an essential prerequisite in the treatment of post-stroke patients with limb spasticity. Most existing spasticity assessment approaches do not consider the muscle activation with voluntary contraction. Including voluntary movements of spastic muscles may provide a new way for the reliable assessment of muscle spasticity.Objective: In this study, we investigated the effectiveness and reliability of maximum isometrics voluntary contraction (MIVC) based method for spasticity assessment in post-stroke hemiplegia.Methods: Fourteen post-stroke hemiplegic patients with arm spasticity were asked to perform two tasks: MIVC and passive isokinetic movements. Three biomechanical signals, torque, position, and time, were recorded from the impaired and non-impaired arms of the patients. Three features, peak torque, keep time of the peak torque, and rise time, were computed from the recorded MIVC signals and used to evaluate the muscle voluntary activation characteristics, respectively. For passive movements, two features, the maximum resistance torque and muscle stiffness, were also obtained to characterize the properties of spastic stretch reflexes. Subsequently, the effectiveness and reliability of the MIVC-based spasticity assessment method were evaluated with spearman correlation analysis and intra class correlation coefficients (ICCs) metrics.Results: The results indicated that the keep time of peak torque and rise time in the impaired arm were higher in comparison to those in the contralateral arm, whereas the peak torque in the impaired side was significantly lower than their contralateral arm. Our results also showed a significant positive correlation (r = 0.503, p = 0.047) between the keep time (tk) and the passive resistant torque. Furthermore, a significantly positive correlation was observed between the keep time (tk) and the muscle stiffness (r = 0.653, p = 0.011). Meanwhile, the ICCs for intra-time measurements of MIVC ranged between 0.815 and 0.988 with one outlier.Conclusion: The findings from this study suggested that the proposed MIVC-based approach would be promising for the reliable and accurate assessment of spasticity in post-stroke patients

Advancing the Functionality and Wearability of Robotic Hand Orthoses Towards Activities of Daily Living in Stroke Patients

Post stroke rehabilitation is effective when a large number of motor repetitions are provided to patients. However, conventional physical therapy or traditional desktop-size robot aided rehabilitation do not provide sufficient number of repetitions due to cost and logistical barriers. Our vision is to realize a wearable and functional hand orthosis that could be used outside of controlled, clinical settings, thus allowing for more training repetitions. Furthermore, if such a device can prove effective for Activities of Daily Living (ADLs) while actively worn, this can incentivize patients to increase its use, further enhancing rehabilitative effects. However, in order to provide such clinical benefits, the device must be completely wearable without obtrusive features, and intuitive to control even for non-experts. In this thesis, we thus focus on wearability, functionality, and intuitive intent detection technology for a novel hand robot, and assess its performance when used both as a rehabilitative device and an assistive tool. A fully wearable device must deliver meaningful manipulation capability in small and lightweight package. In this context, we investigate the capability of single-actuator devices to assist whole hand movement patterns through a network of exotendons. Our prototypes combine a single linear actuator (mounted on a forearm splint) with a network of exotendons (routed on the surface of a soft glove). We investigate two possible tendon network configurations: one that produces full finger extension (overcoming flexor spasticity) and one that combines proximal flexion with distal extension at each finger. In experiments with stroke survivors, we measure the force levels needed to overcome various levels of spasticity and to open the hand for grasping using the first of these configurations, and qualitatively demonstrate the ability to execute fingertip grasps using the second. Our results support the feasibility of developing future wearable devices able to assist a range of manipulation tasks. In order to further improve the wearability of the device, we propose two designs that provide effective force transmission by increasing moment arms around finger joints. We evaluate the designs with geometric models and experiment using a 3D-printed artificial finger to find force and joint angle characteristics of the suggested structures. We also perform clinical tests with stroke patients to demonstrate the feasibility of the designs. The testing supports the hypothesis that the proposed designs efficiently elicit extension of the digits in patients with spasticity as compared to existing baselines. With the suggested transmission designs, the device can deliver sufficient extension force even when the users have increased muscle tone due to fatigue. The vision of an orthotic device used for ADLs can only be realized if the patients are able to operate the device themselves. However, the field is generally lacking effective methods by which the user can operate the device: such controls must be effective, intuitive, and robust to the wide range of possible impairment patterns. The variety of encountered upper limb impairment patterns in stroke patients means that a single sensing modality, such as electromyography, might not be sufficient to enable controls for a broad range of users. To address this significant gap, we introduce a multimodal sensing and interaction paradigm for an active hand orthosis. In our proof-of-concept implementation, EMG is complemented by other sensing modalities, such as finger bend and contact pressure sensors. We propose multimodal interaction methods that utilize this sensory data as input, and show they can enable tasks for stroke survivors who exhibit different impairment patterns. We then assess the performance of the robotic orthosis for two possible roles: as a therapeutic tool that facilitates device mediated hand exercises to recover neuromuscular function, or as an assistive device for use in everyday activities to aid functional use of the hand. 11 chronic stroke (> 2 years) patients with moderate muscle tone (Modified Ashworth Scale ≤ 2 in upper extremity) engage in a month-long training protocol using the orthosis. Individuals are evaluated using standardized outcome measures, both with and without orthosis assistance. The results highlight the potential for wearable and user-driven robotic hand orthoses to extend the use and training of the affected upper limb after stroke. The advances proposed in this thesis have the potential to enable robotic based hand rehabilitation during daily activities (as opposed to isolated hand exercises with limited upper limb engagement) and over extended periods of time, even in a patient’s home environment. Numerous challenges must still be overcome in order to achieve this vision, related to design (compact devices with easier donning/doffing), control (robust yet intuitive intent inferral), and effectiveness (improved functionality in a wider range of metrics). However, if these challenges can be addressed, wearable robotic devices have the potential to greatly extend the use and training of the affected upper limb after stroke, and help improve the quality of life for a large patient population

Wearable fusion system for assessment of motor function in lesion-symptom mapping studies

Lesion-symptom mapping studies are a critical component of addressing the relationship between brain and behaviour. Recent developments have yielded significant improvements in the imaging and detection of lesion profiles, but the quantification of motor outcomes is still largely performed by subjective and low-resolution standard clinical rating scales. This mismatch means than lesion-symptom mapping studies are limited in scope by scores which lack the necessary accuracy to fully quantify the subcomponents of motor function. The first study conducted aimed to develop a new automated system of motor function which addressed the limitations inherent in the clinical rating scales. A wearable fusion system was designed that included the attachment of inertial sensors to record the kinematics of upper extremity. This was combined with the novel application of mechanomyographic sensors in this field, to enable the quantification of hand/wrist function. Novel outputs were developed for this system which aimed to combine the validity of the clinical rating scales with the high accuracy of measurements possible with a wearable sensor system. This was achieved by the development of a sophisticated classification model which was trained on series of kinematic and myographic measures to classify the clinical rating scale. These classified scores were combined with a series of fine-grained clinical features derived from higher-order sensor metrics. The developed automated system graded the upper-extremity tasks of the Fugl-Meyer Assessment with a mean accuracy of 75\% for gross motor tasks and 66\% for the wrist/hand tasks. This accuracy increased to 85\% and 74\% when distinguishing between healthy and impaired function for each of these tasks. Several clinical features were computed to describe the subcomponents of upper extremity motor function. This fine-grained clinical feature set offers a novel means to complement the low resolution but well-validated standardised clinical rating scales. A second study was performed to utilise the fine-grained clinical feature set calculated in the previous study in a large-scale region-of-interest lesion-symptom mapping study. Statistically significant regions of motor dysfunction were found in the corticospinal tract and the internal capsule, which are consistent with other motor-based lesion-symptom mapping studies. In addition, the cortico-ponto-cerebellar tract was found to be statistically significant when testing with a clinical feature of hand/wrist motor function. This is a novel finding, potentially due to prior studies being limited to quantifying this subcomponent of motor function using standard clinical rating scales. These results indicate the validity and potential of the clinical feature set to provide a more detailed picture of motor dysfunction in lesion-symptom mapping studies.Open Acces

Physical Diagnosis and Rehabilitation Technologies

The book focuses on the diagnosis, evaluation, and assistance of gait disorders; all the papers have been contributed by research groups related to assistive robotics, instrumentations, and augmentative devices

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

Implementation of User-Independent Hand Gesture Recognition Classification Models Using IMU and EMG-based Sensor Fusion Techniques

According to the World Health Organization, stroke is the third leading cause of disability. A common consequence of stroke is hemiparesis, which leads to the impairment of one side of the body and affects the performance of activities of daily living. It has been proven that targeting the motor impairments as early as possible while using wearable mechatronic devices as a robot assisted therapy, and letting the patient be in control of the robotic system can improve the rehabilitation outcomes. However, despite the increased progress on control methods for wearable mechatronic devices, the need for a more natural interface that allows for better control remains. This work presents, a user-independent gesture classification method based on a sensor fusion technique that combines surface electromyography (EMG) and an inertial measurement unit (IMU). The Myo Armband was used to measure muscle activity and motion data from healthy subjects. Participants were asked to perform 10 types of gestures in 4 different arm positions while using the Myo on their dominant limb. Data obtained from 22 participants were used to classify the gestures using 4 different classification methods. Finally, for each classification method, a 5-fold cross-validation method was used to test the efficacy of the classification algorithms. Overall classification accuracies in the range of 33.11%-72.1% were obtained. However, following the optimization of the gesture datasets, the overall classification accuracies increased to the range of 45.5%-84.5%. These results suggest that by using the proposed sensor fusion approach, it is possible to achieve a more natural human machine interface that allows better control of wearable mechatronic devices during robot assisted therapies

Role of Sensation in Altered Phalanx Grip Force in Persons with Stroke

Many individuals experience hand impairment after stroke leading to decreased ability to perform daily living activities. Previous research studies have investigated how stroke survivors\u27 pinch grip control differs from healthy individuals, even though many individuals can only grasp with power grip after stroke. Furthermore, many stroke survivors experience tactile sensory deficit in their paretic limb in addition to motor deficit. It is currently unknown how stroke induced tactile sensory deficit affects power grip force directional control, which is important in terms of preventing object slippage and power grip normal force generation. Additionally it is unknown if power grip could be improved through tactile sensory enhancement. This dissertation investigated how stroke survivors\u27 power grip force control is different from healthy individuals. Also, the effect of stroke induced tactile sensory deficit on power grip force control and the benefits of a sensory enhancement method using remote subsensory vibrotactile noise on power grip phalanx force deviation was assessed. In addition, the effect of noise on the tactile sensation for stroke survivors with tactile sensory deficit and their performance on two dynamic gripping tasks, the Box and Block Test (`BBT\u27, number of blocks moved in 60 seconds) and the Nine Hole Peg Test (`NHPT\u27, time to pick up, place, and remove 9 pegs from 9 holes), were investigated. The theoretical framework of this dissertation is that tactile sensation is critical for grip control and impairment or enhancement of tactile sensation impacts power grip force control post stroke. Results showed that stroke survivors, especially those with tactile sensory deficit, gripped with increased phalanx force deviation compared to healthy individuals, showing reduced directional force control and increasing their chances of dropping objects. Remote subsensory vibrotactile noise improved fingertip and upper palm tactile sensation for stroke survivors with tactile sensory deficit. The noise also improved phalanx force directional control during power grip (reducing phalanx force deviation) for stroke survivors with and without tactile sensory deficit and age-matched healthy controls and improved the BBT score and time to complete the NHPT for stroke survivors with tactile sensory deficit. Overall, stroke survivors, particularly those with tactile sensory deficit, appear to have reduced phalanx force control during power grip, which may biomechanically result from a muscle activation pattern. Remote subsensory vibrotactile noise may have enhanced tactile sensation and hand motor control via stochastic resonance and interneuronal connections and could have potential as a wearable rehabilitation device for stroke survivors. This dissertation contributes to the long term goal of increasing stroke survivors\u27 independence in completing daily living activities