International Conference on NeuroRehabilitation 2012

This volume 3, number 2 gathers a set of articles based on the most outstanding research on accessibility and disability issues that was presented in the International Conference on NeuroRehabilitation 2012 (ICNR).The articles’ research present in this number is centred on the analysis and/or rehabilitation of body impairment most due to brain injury and neurological disorders.JACCES thanks the collaboration of the ICNR members and the research authors and reviewers that have collaborated for making possible that issue

Recent Advances in Motion Analysis

The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application

Automatic Posture and Movement Tracking of Infants with Wearable Movement Sensors

Infants' spontaneous and voluntary movements mirror developmental integrity of brain networks since they require coordinated activation of multiple sites in the central nervous system. Accordingly, early detection of infants with atypical motor development holds promise for recognizing those infants who are at risk for a wide range of neurodevelopmental disorders (e.g., cerebral palsy, autism spectrum disorders). Previously, novel wearable technology has shown promise for offering efficient, scalable and automated methods for movement assessment in adults. Here, we describe the development of an infant wearable, a multi-sensor smart jumpsuit that allows mobile accelerometer and gyroscope data collection during movements. Using this suit, we first recorded play sessions of 22 typically developing infants of approximately 7 months of age. These data were manually annotated for infant posture and movement based on video recordings of the sessions, and using a novel annotation scheme specifically designed to assess the overall movement pattern of infants in the given age group. A machine learning algorithm, based on deep convolutional neural networks (CNNs) was then trained for automatic detection of posture and movement classes using the data and annotations. Our experiments show that the setup can be used for quantitative tracking of infant movement activities with a human equivalent accuracy, i.e., it meets the human inter-rater agreement levels in infant posture and movement classification. We also quantify the ambiguity of human observers in analyzing infant movements, and propose a method for utilizing this uncertainty for performance improvements in training of the automated classifier. Comparison of different sensor configurations also shows that four-limb recording leads to the best performance in posture and movement classification.Peer reviewe

A systematic review of game technologies for pediatric patients

[EN] Children in hospital are subjected to multiple negative stimuli that may hinder their development and social interactions. Although game technologies are thought to improve children's experience in hospital, there is a lack of information on how they can be used effectively. This paper presents a systematic review of the literature on the existing approaches in this context to identify gaps for future research. A total of 1305 studies were identified, of which 75 were thoroughly analyzed according to our review protocol. The results show that the most common approach is to design mono-user games with traditional computers or monitor-based video consoles, which serve as a distractor or a motivator for physical rehabilitation for primary school children undergoing fearful procedures such as venipuncture, or those suffering chronic, neurological, or traumatic diseases/injures. We conclude that, on the one hand, game technologies seem to present physical and psychological benefits to pediatric patients, but more research is needed on this. On the other hand, future designers of games for pediatric hospitalization should consider: 1. The development for kindergarten patients and adolescents, 2. Address the psychological impact caused by long-term hospitalization, 3. Use collaboration as an effective game strategy to reduce patient isolation, 4. Have purposes other than distraction, such as socialization, coping with emotions, or fostering physical mobility, 5. Include parents/caregivers and hospital staff in the game activities; and 6. Exploit new technological artifacts such as robots and tangible interactive elements to encourage intrinsic motivation.This work is supported by the Spanish Ministry of Economy and Competitiveness and the European Development Regional Fund (EDRF-FEDER) with Project TIN2014-60077-R.El Jurdi, S.; Montaner-Marco, J.; García Sanjuan, F.; Jaén Martínez, FJ.; Nácher-Soler, VE. (2018). A systematic review of game technologies for pediatric patients. Computers in Biology and Medicine. 97:89-112. https://doi.org/10.1016/j.compbiomed.2018.04.019S891129

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Wearables for Movement Analysis in Healthcare

Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients’ capabilities, unobtrusively and continuously, for clinical purposes

Deep learning for gait prediction: an application to exoskeletons for children with neurological disorders

Cerebral Palsy, a non-progressive neurological disorder, is a lifelong condition. While it has no cure, clinical intervention aims to minimise the impact of the disability on individuals' lives. Wearable robotic devices, like exoskeletons, have been rapidly advancing and proving to be effective in rehabilitating individuals with gait pathologies. The utilization of artificial intelligence (AI) algorithms in controlling exoskeletons, particularly at the supervisory level, has emerged as a valuable approach. These algorithms rely on input from onboard sensors to predict gait phase, user intention, or joint kinematics. Using AI to improve the control of robotic devices not only enhances human-robot interaction but also has the potential to improve user comfort and functional outcomes of rehabilitation, and reduce accidents and injuries. In this research study, a comprehensive systematic literature review is conducted, exploring the various applications of AI in lower-limb robotic control. This review focuses on methodological parameters such as sensor usage, training demographics, sample size, and types of models while identifying gaps in the existing literature. Building on the findings of the review, subsequent research leveraged the power of deep learning to predict gait trajectories for the application of rehabilitative exoskeleton control. This study addresses a gap in the existing literature by focusing on predicting pathological gait trajectories, which exhibit higher inter- and intra-subject variability compared to the gait of healthy individuals. The research focused on the gait of children with neurological disorders, particularly Cerebral Palsy, as they stand to benefit greatly from rehabilitative exoskeletons. State-of-the-art deep learning algorithms, including transformers, fully connected neural networks, convolutional neural networks, and long short-term memory networks, were implemented for gait trajectory prediction. This research presents findings on the performance of these models for short-term and long-term recursive predictions, the impact of varying input and output window sizes on prediction errors, the effect of adding variable levels of Gaussian noise, and the robustness of the models in predicting gait at speeds within and outside the speed range of the training set. Moreover, the research outlines a methodology for optimising the stability of long-term forecasts and provides a comparative analysis of gait trajectory forecasting for typically developing children and children with Cerebral Palsy. A novel approach to generating adaptive trajectories for children with Cerebral Palsy, which can serve as reference trajectories for position-controlled exoskeletons, is also presented

Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

This Special Issue shows a range of potential opportunities for the application of wearable movement sensors in motor rehabilitation. However, the papers surely do not cover the whole field of physical behavior monitoring in motor rehabilitation. Most studies in this Special Issue focused on the technical validation of wearable sensors and the development of algorithms. Clinical validation studies, studies applying wearable sensors for the monitoring of physical behavior in daily life conditions, and papers about the implementation of wearable sensors in motor rehabilitation are under-represented in this Special Issue. Studies investigating the usability and feasibility of wearable movement sensors in clinical populations were lacking. We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation

Evaluation of an Actuated Wrist Orthosis for Use in Assistive Upper Extremity Rehabilitation

Cerebral palsy (CP) is a neurological condition caused by damage to motor control centers of the brain. This leads to physical and cognitive deficiencies that can reduce an individual’s quality of life. Specifically, motor deficiencies of the upper extremity can make it difficult for an individual to complete everyday tasks, including eating, drinking, getting dressed, or combing their hair. Physical therapy, involving repetitive tasks, has been shown to be effective in training normal motion of the limb by invoking the neuroplasticity of the brain and its ability to adapt in order to facilitate motor learning. Creating a device for use with Activities of Daily Living (ADLs) provides an additional tool for task-based therapy with the goal of improving functional outcome. A custom wrist orthotic has been designed and developed that assists flexion/extension of the wrist and rotation of the forearm, while leaving the hand open for the grasp and manipulation of objects. Actuated joints are driven with geared brushless DC motors on a lightweight, exoskeleton frame coupled to a passive arm that tracks positional changes within the task space. Control of actuation is accomplished with a custom mapping strategy, created from nominal movement profiles for 5 ADLs collected from healthy subjects. A simple relationship was created between position within the workspace and orientation necessary for task completion to determine needed assistance. Validation of the design subjected the device to three different conditions, including robot guidance of the limb, co-contraction of the forearm, and the use of alternate approaches to complete the task. Co-contraction and alternate approach conditions were used to simulate characteristics of impaired subjects, including rigidity spasticity, and lack of muscle control. Robot guidance achieved an average orientation error of 5° or less in at least 75% of iterations across all tasks, while co-contraction and alternate approach was able to do this in flexion/extension, but saw much higher errors in forearm rotation. Causes for performance deficiencies were attributed to lack of torque bandwidth at the motor and response delay due to signal filtering, aspects that will be corrected in the next iteration of the design