David Ribeiro

Towards a movement quantification system capable of automatic evaluation of upper limb motor function after neurological injury

The paper proposes an integrated system to automatically assess motor function after neurological injury. A portable motion capture system was developed in order to obtain all the relevant three dimensional kinematics of the upper limb movement. These kinematics were analyzed by means of a decision tree classifier which features where inferred from the Functional Ability Score (FAS) of the Wolf Motor Function Test (WMFT). In addition, the system is able to correctly quantify the performance time of each selected task of the WMFT. In terms of the FAS the system and the clinician show coherent results for 3 out of 5 patients in the first task tested and 4 out of 5 for the second task tested. Regarding performance time, the mean error between the system and the clinician was of 0.216 s for the 25 trials performed (5 patients, 5 tasks each). These results represent an important proof of concept towards a system capable of precisely evaluate upper limb motor function after neurological injury.

A Novel Dry Active Biosignal Electrode Based on an Hybrid Organic-Inorganic Interface Material

In this paper, we describe the design, implementation and testing of a dry active flexible electrode with a novel interface material for wearable biosignal recording. The new interface material takes the form of a gel and is highly bendable and comfortable on the wearers skin. A comparison between common Ag/AgCl and our dry active electrode was performed on seven healthy volunteers. The presented prototype was designed for ECG signals but this technology can be modified for other biosignals. Our results show that the new dry active electrode presents better electrical characteristics than the common Ag/AgCl electrode, namely less power-line interference and better response in the signal band. We can conclude that our novel dry active flexible electrode outperforms the traditional Ag/AgCl wet electrode with the advantages of being dry and comfortable. Some future applications of this bio device are discussed.

The vibratory stimulus as a neurorehabilitation tool for stroke patients: Proof of concept and tolerability test

INTRODUCTION Current scientific knowledge reinforces that successful reorganization of surviving nervous tissue supports cognitive and motor recovery after stroke. The development of new neurorehabilitation tools to modulate this physiologic process is needed. In this context, vibratory stimuli are a noninvasive form of proprioceptive stimulation of the nervous system and are freely available and easy to use at a low cost. OBJECTIVES To access the feasibility and tolerability of targeted vibratory stimuli delivered through a wearable device in an early post-stroke setting. PATIENTS AND METHODS Five stroke patients were recruited from a stroke unit setting having a first ever medial cerebral artery ischemic stroke with motor deficit. The stimulation device developed delivered external vibratory stimuli to major joints at preprogrammed arrays of intensity, duration and interval of actuation. The tolerability test was set for five-hour duration and during that period data on vital parameters, cognitive, motor and sensitive performance as well as anxiety scores were recorded. RESULTS The device remained in place throughout and none of the patients or relatives asked to interrupt the tolerability test. There were no major complications during the trial or the ensuing days. Attention to the affected side during stimulation was increased in four patients, and two were reported as clearly more awake during the test. DISCUSSION This is the first tolerability test focused on the use of targeted vibratory stimulus as a neurorehabilitation tool in stroke patients. There were no hazards to report and most interestingly the majority of patients showed increased awareness to the affected side of the body. These findings will be further analyzed under functional MRI control and on long-term ambulatory use trials.

A real time, wearable ECG and continous blood pressure monitoring system for first responders

The study of stress and fatigue among First Responders is a major step in mitigating this public health problem. Blood pressure, heart rate variability and fatigue related arrhythmia are three of the main “windows” to study stress and fatigue. In this paper we present a wearable medical device, capable of acquiring an electrocardiogram and estimating blood pressure in real time, through a pulse wave transit time approach. The system is based on an existent certified wearable medical device called “Vital Jacket” and is aimed to become a tool to allow cardiologists in studying stress and fatigue among first response professionals.

A novel system for automatic classification of upper limb motor function after stroke: An exploratory study

In the early post-stroke phase, when clinicians attempt to evaluate interventions and accurately measure motor performance, reliable tools are needed. Therefore, the development of a system capable of independent, repeated and automatic assessment of motor function is of increased importance. This manuscript explores the potential of a newly designed device for automatic assessment of motor impairment after stroke. A portable motion capture system was developed to acquire three-dimensional kinematics data of upper limb movements. These were then computed through an automatic decision tree classifier, with features inferred from the Functional Ability Score (FAS) of the Wolf Motor Function Test (WMFT). Five stroke patients were tested on both sides across five selected tasks. The system was compared against a trained clinician, operating simultaneously and blinded. Regarding performance time, the mean difference (system vs clinician) was 0.17s (sd=0.14s). For FAS evaluation, there was agreement in 4 out of 5 patients in the two tasks evaluated. The prototype tested was able to automatically classify upper limb movement, according to a widely used functional motor scale (WMFT) in a relevant clinical setting. These results represent an important step towards a system capable of precise and independent motor evaluation after stroke. The portability and low-cost design will contribute for its usability in ambulatory clinical settings and research trials.

A smartphone-based fall risk assessment tool: Measuring One Leg Standing, Sit to Stand and Falls Efficacy Scale

Falls are not an inevitable consequence of ageing. Several fall risk factors can be identified and effective fall prevention techniques applied, which offer an opportunity to reduce falls among older persons. In this paper, the smartphone is proposed as an alternative to traditional methods in the assessment of fall risk factors, including decline in balance, reduced lower limb strength and fear of falling. As such, clinical fall risk assessment tests were adapted to the smartphone in order to measure One Leg Standing, Sit to Stand and Falls Efficacy Scale. Experimental results of the system support the feasibility of a reliable phone-based fall predictor, which constitutes an alternative to evaluate fall risk factors in ageing.

Motor task performance under vibratory feedback early poststroke: single center, randomized, cross-over, controled clinical trial

Stroke rehabilitation is far from meeting patient needs in terms of timing, intensity and quality. This study evaluates the efficacy and safety of an innovative technological tool, combining 3D motion analysis with targeted vibratory feedback, on upper-limb task performance early poststroke (<4 weeks). The study design was a two-sequence, two-period, randomized, crossover trial (NCT01967290) in 44 patients with upper-limb motor deficit (non-plegic) after medial cerebral artery ischemia. Participants were randomly assigned to receive either the experimental session (repetitive motor task under vibratory feedback and 3D motor characterization) or the active comparator (3D motor characterization only). The primary outcome was the number of correct movements per minute on a hand-to-mouth task measured independently. Vibratory feedback was able to modulate motor training, increasing the number of correct movements by an average of 7.2/min (95%CI [4.9;9.4]; P < 0.001) and reducing the probability of performing an error from 1:3 to 1:9. This strategy may improve the efficacy of training on motor re-learning processes after stroke, and its clinical relevance deserves further study in longer duration trials.

A smartphone-based fall risk assessment tool: Testing Ankle Flexibility, Gait and Voluntary Stepping

Falls are a frequent occurrence in elderly population, contributing significantly to injuries and decreased quality of life. However, through evidence-based interventions for fall prevention, specific risk factors can be modified, offering an opportunity to reduce the number of falls among older persons. An enhanced knowledge of the nature of the risks is of paramount importance to enable a proper design of preventive schemes. In this paper, indicators of declines in balance and mobility are evaluated using a smartphone-based system in alternative to traditional methods such as force platforms and cameras. Fall risk assessment tests were adapted to the smartphone enabling it to test Ankle Flexibility, Gait and Voluntary Stepping. Experimental results show a good correlation of most of the variables derived from the tests using the smartphone and force platforms or cameras. In conclusion, the implementation of these tests in a smartphone platform to measure fall risk-related features supports the feasibility of a valuable alternative to the traditional technology used in dedicated laboratories for the analysis of movement.

The potential of motion quantification systems in the automatic evaluation of motor function after stroke.

The assessment of motor performance is of major importance for correct decision making in neurorehabilitation, especially early after stroke (1). The need for more reliable and valid tools of motor assessment is common to both clinical and research settings. In addition, everyday clinical practice is usually disturbed by scarcity of specialized human resources, which limits the time dedicated to motor assessment and the number of possible motor tests performed. In this context, a wearable system capable of automatic assessment of motor function is of increased importance. It could allow clinicians to easily document motor performance and would represent a significant upgrade in the management of future rehabilitation plans and clinical trials (2). To achieve this goal, we developed a portable motion capture system based on magnetic, angular rate, and gravity sensors to acquire all the relevant threedimensional kinematics of upper limb movements (3). These data were then computed through an automatic decision tree classifier, whose features were inferred from the Functional Ability Score (FAS) of the Wolf Motor Function Test. Features comprised the analysis of synergic movements with the shoulder, smoothness, and motor executions out of the plane of action. Five stroke patients were enrolled and tested on both sides in five selected tasks. The system was compared against a trained clinician, operating simultaneously and blinded. Results showed, in terms of performance time, a mean difference between the system and the clinician of 0·17 s (standard deviation = 0·14 s) for all trials performed (3). The systematic delay of the clinician’ assessments were probably due to human error, when indicating task conclusion time. For FAS evaluation, the system and the clinician showed agreement in four out of five patients for two motor tasks evaluated (3). These results represent an important step toward a system capable of a precise and independent motor evaluation after stroke. Virgílio F. Bento, Vítor T. Cruz*, David D. Ribeiro, Catarina Branco, and Paula Coutinho

Supervised learning for Out-of-Stock detection in panoramas of retail shelves

Improving inventory management is essential to retailer profitability. This paper proposes a supervised learning approach for Out-of-Stock (OOS) detection by Texture, Color and Geometry features in high-resolution panoramic images of grocery retail shelves. Cascade classifiers are used to detect labels that can potentially be used to confirm the presence of the OOS cases. The image acquisition setup includes a camera cart that shoots from multi-viewpoints aiming a parallel motion to the shelf. The correction of perspective distortion is applied to handle the different camera translation motions while stitching together images with a high-level of similarity. From the generated panoramas, the proposed OOS detection is followed by classification with Support Vector Machines. The experimental tests were performed throughout the retail environment with real data obtained from supermarket shelves containing labels near the visible ruptures. Results show a detection accuracy of 84.5% for OOS and a sensitivity of 86.6% for label detection.