Mobile Quantification and Therapy Course Tracking for Gait Rehabilitation

This paper presents a novel autonomous quality metric to quantify the rehabilitations progress of subjects with knee/hip operations. The presented method supports digital analysis of human gait patterns using smartphones. The algorithm related to the autonomous metric utilizes calibrated acceleration, gyroscope and magnetometer signals from seven Inertial Measurement Unit attached on the lower body in order to classify and generate the grading system values. The developed Android application connects the seven Inertial Measurement Units via Bluetooth and performs the data acquisition and processing in real-time. In total nine features per acceleration direction and lower body joint angle are calculated and extracted in real-time to achieve a fast feedback to the user. We compare the classification accuracy and quantification capabilities of Linear Discriminant Analysis, Principal Component Analysis and Naive Bayes algorithms. The presented system is able to classify patients and control subjects with an accuracy of up to 100\%. The outcomes can be saved on the device or transmitted to treating physicians for later control of the subject's improvements and the efficiency of physiotherapy treatments in motor rehabilitation. The proposed autonomous quality metric solution bears great potential to be used and deployed to support digital healthcare and therapy.Comment: 5 Page

Bodily Sensory Inputs and Anomalous Bodily Experiences in Complex Regional Pain Syndrome: Evaluation of the Potential Effects of Sound Feedback

Neuroscientific studies have shown that human's mental body representations are not fixed but are constantly updated through sensory feedback, including sound feedback. This suggests potential new therapeutic sensory approaches for patients experiencing body-perception disturbances (BPD). BPD can occur in association with chronic pain, for example in Complex Regional Pain Syndrome (CRPS). BPD often impacts on emotional, social, and motor functioning. Here we present the results from a proof-of-principle pilot study investigating the potential value of using sound feedback for altering BPD and its related emotional state and motor behavior in those with CRPS. We build on previous findings that real-time alteration of the sounds produced by walking can alter healthy people's perception of their own body size, while also resulting in more active gait patterns and a more positive emotional state. In the present study we quantified the emotional state, BPD, pain levels and gait of twelve people with CRPS Type 1, who were exposed to real-time alteration of their walking sounds. Results confirm previous reports of the complexity of the BPD linked to CRPS, as participants could be classified into four BPD subgroups according to how they mentally visualize their body. Further, results suggest that sound feedback may affect the perceived size of the CRPS affected limb and the pain experienced, but that the effects may differ according to the type of BPD. Sound feedback affected CRPS descriptors and other bodily feelings and emotions including feelings of emotional dominance, limb detachment, position awareness, attention and negative feelings toward the limb. Gait also varied with sound feedback, affecting the foot contact time with the ground in a way consistent with experienced changes in body weight. Although, findings from this small pilot study should be interpreted with caution, they suggest potential applications for regenerating BDP and its related bodily feelings in a clinical setting for patients with chronic pain and BPD

Effects of dance therapy on balance, gait and neuro-psychological performances in patients with Parkinson's disease and postural instability

Postural Instability (PI) is a core feature of Parkinson’s Disease (PD) and a major cause of falls and disabilities. Impairment of executive functions has been called as an aggravating factor on motor performances. Dance therapy has been shown effective for improving gait and has been suggested as an alternative rehabilitative method. To evaluate gait performance, spatial-temporal (S-T) gait parameters and cognitive performances in a cohort of patients with PD and PI modifications in balance after a cycle of dance therapy

Cognitive healthcare system and its application in pill-rolling assessment

Directional antennas have been extensively used in wireless sensor networks (WSNs) for various applications. This work presents the application of a four‐beam patch antenna as a sensor node to assess the pill‐rolling effect in Parkinson disease. The four‐beam patch is small in size, highly directive, and can suppress the multipath fading encountered in indoor settings that adversely affects the measurements. The pill‐rolling effect refers to tremors in the hands, particularly in the forefinger and the thumb, which the patient involuntary rubs together. The core idea is to develop a low‐cost framework that effectively evaluates the particular movement disorder to assist doctors or clinicians in carrying out an objective assessment using the S‐band sensing technique leveraging small wireless devices operating at 2.4 GHz. The proposed framework uses the perturbations in amplitude and phase information to efficiently identify tremors and nontremors experienced in the fingers. The unique imprint induced by each body motion is used to determine the particular body motion disorder. The performance of the framework is evaluated using the support vector machine algorithm. The results indicate that the framework provides high classification accuracy (higher than 90%)

Frailty assessment based on trunk kinematic parameters during walking

Background: Physical frailty has become the center of attention of basic, clinical and demographic research due to its incidence level and gravity of adverse outcomes with age. Frailty syndrome is estimated to affect 20 % of the population older than 75 years. Thus, one of the greatest current challenges in this field is to identify parameters that can discriminate between vulnerable and robust subjects. Gait analysis has been widely used to predict frailty. The aim of the present study was to investigate whether a collection of parameters extracted from the trunk acceleration signals could provide additional accurate information about frailty syndrome. Methods: A total of 718 subjects from an elderly population (319 males, 399 females; age: 75.4 ± 6.1 years, mass: 71.8 ± 12.4 kg, height: 158 ± 6 cm) volunteered to participate in this study. The subjects completed a 3-m walk test at their own gait velocity. Kinematic data were acquired from a tri-axial inertial orientation tracker. Findings: The spatio-temporal and frequency parameters measured in this study with an inertial sensor are related to gait disorders and showed significant differences among groups (frail, pre-frail and robust). A selection of those parameters improves frailty classification obtained to gait velocity, compared to classification model based on gait velocity solely. Interpretation: Gait parameters simultaneously used with gait velocity are able to provide useful information for a more accurate frailty classification. Moreover, this technique could improve the early detection of pre-frail status, allowing clinicians to perform measurements outside of a laboratory environment with the potential to prescribe a treatment for reversing their physical decline.This work was supported in part by the Spanish Department of Health and Institute Carlos III of the Government of Spain [Spanish Net on Aging and frailty; (RETICEF)], and Economy and Competitivity Department of the Government of Spain, under grants numbered RD12/043/0002, and DEP2011-24105, respectively

Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures

The demographic shift of the population towards an increase in the number of elderly citizens, together with the sedentary lifestyle we are adopting, is reflected in the increasingly debilitated physical health of the population. The resulting physical impairments require rehabilitation therapies which may be assisted by the use of wearable sensors or body area network sensors (BANs). The use of novel technology for medical therapies can also contribute to reducing the costs in healthcare systems and decrease patient overflow in medical centers. Sensors are the primary enablers of any wearable medical device, with a central role in eHealth architectures. The accuracy of the acquired data depends on the sensors; hence, when considering wearable and BAN sensing integration, they must be proven to be accurate and reliable solutions. This book is a collection of works focusing on the current state-of-the-art of BANs and wearable sensing devices for physical rehabilitation of impaired or debilitated citizens. The manuscripts that compose this book report on the advances in the research related to different sensing technologies (optical or electronic) and body area network sensors (BANs), their design and implementation, advanced signal processing techniques, and the application of these technologies in areas such as physical rehabilitation, robotics, medical diagnostics, and therapy