Assigning UPDRS Scores in the Leg Agility Task of Parkinsonians: Can It Be Done through BSN-based Kinematic Variables?

In this paper, by characterizing the Leg Agility (LA) task, which contributes to the evaluation of the degree of severity of the Parkinson's Disease (PD), through kinematic variables (including the angular amplitude and speed of thighs' motion), we investigate the link between these variables and Unified Parkinson's Disease Rating Scale (UPDRS) scores. Our investigation relies on the use of a few body-worn wireless inertial nodes and represents a first step in the design of a portable system, amenable to be integrated in Internet of Things (IoT) scenarios, for automatic detection of the degree of severity (in terms of UPDRS score) of PD. The experimental investigation is carried out considering 24 PD patients.Comment: 10 page

Validation of seat-off and seat-on in repeated sit-to-stand movements using a single body fixed sensor

The identification of chair rise phases is a prerequisite for quantifying sit-to-stand movements. The aim of this study is to validate seat-off and seat-on detection using a single-body-fixed sensor against detection based on chair switches. A single sensor system with three accelerometers and three gyroscopes was fixed around the waist. Synchronized on-off switches were placed under the chair. Thirteen older adults were recruited from a residential care home and fifteen young adults were recruited among college students. Subjects were asked to complete two sets of five trials each. Six features of the trunk movement during seat-off and seat-on were calculated automatically, and a model was developed to predict the moment of seat-off and seat-on transitions. The predictions were validated with leave-one-out cross-validation. Feature extraction failed in two trials (0.7%). For the optimal combination of seat-off predictors, cross-validation yielded a mean error of 0ms and a mean absolute error of 51ms. For the best seat-on predictor, cross-validation yielded a mean error of -3ms and a mean absolute error of 127ms. The results of this study demonstrate that seat-off and seat-on in repeated sit-to-stand movements can be detected semi-automatically in young and older adults using a one-body-fixed sensor system with an accuracy of 51 and 127ms, respectively. The use of the ambulatory instrumentation is feasible for non-technically trained personnel. This is an important step in the development of an automated method for the quantification of sit-to-stand movements in clinical practice. © 2012 Institute of Physics and Engineering in Medicine

Validation, optimization and exploitation of orientation measurements issued from inertial systems for clinical biomechanics

Les centrales inertielles (triade de capteurs inertiels dont la fusion des données permet l’estimation de l’orientation d’un corps rigide) sont de plus en plus populaires en biomécanique. Toutefois, les qualités métrologiques des centrales inertielles (CI) sont peu documentées et leur capacité à identifier des incapacités liées à la mobilité, sous-évaluée. Objectifs : (i) Caractériser la validité de la mesure d’orientation issue de CI ; (ii) Optimiser la justesse et la fidélité de ces mesures; et (iii) Proposer des métriques de mobilité basées sur les mesures d’orientation issues de CI. Méthodologie et résultats : La validité de la mesure d’orientation de différents types de CI a d’abord été évaluée en conditions contrôlées, à l’aide d’une table motorisée et d’une mesure étalon. Il a ainsi été démontré que les mesures d’orientation issues de CI ont une justesse acceptable lors de mouvements lents (justesse moyenne ≤ 3.1º), mais que cette justesse se dégrade avec l’augmentation de la vitesse de rotation. Afin d’évaluer l’impact de ces constatations en contexte clinique d’évaluation de la mobilité, 20 participants ont porté un vêtement incorporant 17 CI lors de la réalisation de diverses tâches de mobilité (transferts assis-debout, marche, retournements). La comparaison des mesures des CI avec celles d’un système étalon a permis de dresser un portrait descriptif des variations de justesse selon la tâche exécutée et le segment/l’articulation mesuré. À partir de ces constats, l’optimisation de la mesure d’orientation issue de CI est abordée d’un point de vue utilisateur, démontrant le potentiel d’un réseau de neurones artificiel comme outil de rétroaction autonome de la qualité de la mesure d’orientation (sensibilité et spécificité ≥ 83%). Afin d’améliorer la robustesse des mesures de cinématique articulaire aux variations environnementales, l’ajout d’une photo et d’un algorithme d’estimation de pose tridimensionnelle est proposé. Lors d’essais de marche (n=60), la justesse moyenne de l’orientation à la cheville a ainsi été améliorée de 6.7° à 2.8º. Finalement, la caractérisation de la signature de la cinématique tête-tronc pendant une tâche de retournement (variables : angle maximal tête-tronc, amplitude des commandes neuromusculaires) a démontré un bon pouvoir discriminant auprès de participants âgés sains (n=15) et de patients atteints de Parkinson (PD, n=15). Ces métriques ont également démontré une bonne sensibilité au changement, permettant l’identification des différents états de médication des participants PD. Conclusion : Les mesures d’orientation issues de CI ont leur place pour l’évaluation de la mobilité. Toutefois, la portée clinique réelle de ce type de système ne sera atteinte que lorsqu’il sera intégré et validé à même un outil de mesure clinique.Abstract : Inertial measurement of motion is emerging as an alternative to 3D motion capture systems in biomechanics. Inertial measurement units (IMUs) are composed of accelerometers, gyroscopes and magnetometers which data are fed into a fusion algorithm to determine the orientation of a rigid body in a global reference frame. Although IMUs offer advantages over traditional methods of motion capture, the value of their orientation measurement for biomechanics is not well documented. Objectives: (i) To characterize the validity of the orientation measurement issued from IMUs; (ii) To optimize the validity and the reliability of these measurements; and (iii) To propose mobility metrics based on the orientation measurement obtained from IMUs. Methods and results: The criterion of validity of multiple types of IMUs was characterized using a controlled bench test and a gold standard. Accuracy of orientation measurement was shown to be acceptable under slow conditions of motion (mean accuracy ≤ 3.1º), but it was also demonstrated that an increase in velocity worsens accuracy. The impact of those findings on clinical mobility evaluation was then assessed in the lab, with 20 participants wearing an inertial suit while performing typical mobility tasks (standing-up, walking, turning). Comparison of the assessed IMUs orientation measurements with those from an optical gold standard allowed to capture a portrait of the variation in accuracy across tasks, segments and joints. The optimization process was then approached from a user perspective, first demonstrating the capability of an artificial neural network to autonomously assess the quality of orientation data sequences (sensitivity and specificity ≥ 83%). The issue of joint orientation accuracy in magnetically perturbed environment was also specifically addressed, demonstrating the ability of a 2D photograph coupled with a 3D pose estimation algorithm to improve mean ankle orientation accuracy from 6.7° to 2.8º when walking (n=60 trials). Finally, characterization of the turn cranio-caudal kinematics signature (variables: maximum head to trunk angle and neuromuscular commands amplitude) has demonstrated a good ability to discriminate between healthy older adults (n=15) and early stages of Parkinson’s disease patients (PD, n=15). Metrics have also shown a good sensitivity to change, enabling to detect changes in PD medication states. Conclusion: IMUs offer a complementary solution for mobility assessment in clinical biomechanics. However, the full potential of this technology will only be reached when IMUs will be integrated and validated within a clinical tool

Ergonomics

The accuracy and repeatability of an inertial measurement unit (IMU) system for directly measuring trunk angular displacement and upper arm elevation were evaluated over eight hours (i) in comparison to a gold standard, optical motion capture (OMC) system in a laboratory setting, and (ii) during a field-based assessment of dairy parlour work. Sample-to-sample root mean square differences between the IMU and OMC system ranged from 4.1\ub0 to 6.6\ub0 for the trunk and 7.2\ub0-12.1\ub0 for the upper arm depending on the processing method. Estimates of mean angular displacement and angular displacement variation (difference between the 90th and 10th percentiles of angular displacement) were observed to change\ua0<4.5\ub0 on average in the laboratory and\ua0<1.5\ub0 on average in the field per eight hours of data collection. Results suggest the IMU system may serve as an acceptable instrument for directly measuring trunk and upper arm postures in field-based occupational exposure assessment studies with long sampling durations. Practitioner Summary: Few studies have evaluated inertial measurement unit (IMU) systems in the field or over long sampling durations. Results of this study indicate that the IMU system evaluated has reasonably good accuracy and repeatability for use in a field setting over a long sampling duration.2022-09-13T00:00:00Z26256753PMC946963411892vault:4327

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

Developing a methodology to perform measurements of the multi-spinal regions and lumbar-hip complex kinematics during dominant daily tasks

Introduction: Quantitative data of spinal range of motion in vivo is essential to improve clinicians’ understanding of spinal pathologies, procedure of assessment and treatment. Accurate knowledge of physiological movement of lumbar spine regions, hip and the behaviour of each regional movement is important. Spine and hip motion play an essential role in daily functional activities, such as self-caring or performing occupational duties. Measuring the regional breakdown of spinal motion in three planes and describing the relative motion of different regions of the thoracolumbar (TL) spine can provide useful clinical information, which can be used in clinical procedure for spinal assessment. The relationship between the forward flexion (i.e. cardinal motion) and more functional tasks, such as lifting, stand-to-sit and sit-to-stand, as well as dividing the lumbar spine into more than one region, relative to the hip during these tasks, have not yet been established. Measuring the regional breakdown of spinal motions in three planes, as well as the relationship between lumbar spine and hip in sagittal plane, requires a multi-regional analysis system. Aims and objectives: The fundamental aim was to explore range of motion and velocity magnitudes in flexion, extension lifting, stand-to-sit and sit-to-stand tasks, using three lumbar regions relative to the hip, and to determine correlations and differences between flexion and other dominant functional tasks. An objective was to obtain an appropriate measurement system that is capable of measuring dynamic movement in ‘real time’ and examine its validity against a “gold standard” system and its reliability, by measuring the range of motion of multi-spinal regions. Also, to demonstrate the relative contribution of five regions from within the thoracolumbar and head-cervical regions in 3D. Methods: The selected system (tri-axial accelerometer sensors-(3A sensors)) was validated against a “gold standard” system (roll table (RT)) to demonstrate a correlation and root mean square errors (RMSEs) between the two devices. Reliability of the 3A sensors and the contribution of multi-spinal regions was assessed on 18 healthy participants. Two protocols were applied: in protocol one, two sensors were placed on the forehead and T1, to measure cervical ROM; in protocol two, six sensors were placed on the spinous processes of T1, T4, T8, T12, L3 and S1 to measure thoraco-lumbar regional range of motion. It also divided the lumbar spine as one single joint (S1 to T12) and as two regions (the upper (T12-L3) and lower (L3-S1)) and hip region. Data was gathered from 53 participants using four sensors attached to the skin over the S1, L3, T12 and lateral thigh. Two different statistical analyses were applied: one for analysing each particular region’s contribution, relative to the hip; and another to analyse the correlation between the kinematic profiles of flexion and three sagittally dominant functional tasks (lifting, stand-to-sit and sit-to-stand). Results: Validation of 3A accelerometer sensors system against the roll table revealed a strong correlation between the two systems average (ICC=.998 (95% CI=.993-.999)) and an acceptable rate of errors ranged from (2.54º (0.70%) to 5.01º (1.39%). It also demonstrated the reliability of this system, when the ICC values for all regions were high with relatively small errors associated with a novel multi-regional clinical spinal motion system. The ICC values for all regions were found to be high, ranging from .88 and .99 with 95% CI ranged from .62 to .99 while errors values ranged from 0.4 to 5.2°. The additional movement information, gathered from a multi-regional breakdown, adds insight into the relative contributions to spinal movement. Significant differences existed between ROM of LLS and ULS across all movements (p<0.05). A significant difference also existed between ULS-hip and LLS-hip ratio for the majority of tasks (p<0.05), and between ULS and LLS velocity for the majority of tasks (p<0.05). The findings from the lumbar spine as one region, underestimates the contribution of the lower lumbar and overestimates the contribution of the upper lumbar spine. Strong correlations for ROM are reported between forward flexion tasks and lifting for the LL spine (r = 0.83) and all regions during stand-to-sit and sit-to-stand (r = 0.70-0.73). No tasks were strongly correlated for velocity (r = 0.03-0.55). Conclusion: The validity and reliability of the accelerometer sensors system is evidence of its ability to measure spinal movement. Since it is inexpensive, small, portable and relatively easy to use, it could be a preferable system for clinical application. The data, from multi-spinal regions, provides a novel method for practitioners to focus on a greater number of regions, rather than measuring only the three main areas of the spine (cervical, thoracic and lumbar). Investigating the lumbar spine as only one region risks missing out important kinematic detail. Further, the methodology provides the potential to measure functionally unique kinematics from more complex functional tasks, rather than generalised findings from clinical assessments of simple flexion

Inertial Sensing for Human Motion Analysis: Processing, Technologies, and Applications

Human motion has always attracted significant interest and curiosity. In particular, the last two centuries have seen a fast and great development of innovative techniques and technologies for the scientific analysis of human motion. If initially this was mainly due to the large interest in biomedical fields, a growing number of other leading applications has kept this interest alive until today. These applications emerge, for instance, in sport, entertainment, and industrial contexts. The first motion capture systems, appeared along the nineteenth century, were typically based on optical technologies and their development was profoundly interlaced with the contemporary development of photography and cinematography. Since then, many other different technologies have been employed to develop new motion capture systems, such as (but not limited to) inertial, mechanical, magnetic, and acoustic. In particular, inertial motion capture systems, based on the use of inertial sensors (such as the accelerometer, which measures the acceleration, and the gyroscope, which measures angular velocity), are likely to replace the previous ones and become a standard technology. This is mainly favored by the recent great improvement in the large-scale development of accurate inertial sensors ever cheaper. When referring to inertial human motion analysis, several application areas are driving current research and development efforts. A tentative list may include, for instance, the following: clinical and home monitoring and/or rehabilitation; ambient assisted living; computer graphics and computer animation; gaming and virtual reality; sport training; pedestrian navigation; and robotics. Furthermore, human motion analysis often implies a transversal investigation of many aspects of human motion, at different levels of abstraction and at different detail depths. For instance, one may just be interested in recognizing and estimating the pose of a person as well as in identifying the activities and/or the gestures that he/she is performing. Furthermore, one may be just interested in analyzing a restricted part of the body rather than focusing on the full body. Due to this heterogeneity of topics and intents, this thesis does not focus on a specific application or method, but aims at investigating different aspects of inertial human motion analysis, by specifically discussing the corresponding data processing approaches and the involved technologies. Four research areas have been taken into account which correspond to four types of applications: arm posture recognition; activity classification; evaluation of functional motor tasks; and motion reconstruction. In particular, these applications have been chosen in order to cover topics with different levels of abstraction and different detail depths