The Automated Assessment of Postural Stability: Balance Detection Algorithm

International Classification of Functioning, Disability, and Health augmented by telemedicine and artificial intelligence for assessment of functional disability

The concept of functional disability is aligned with the biopsycho-social model of disability. However, there are reasons why the antiquated measurement of medical impairment continues to be in use. We propose solutions for a fairer process using the International Classification of Functioning, Disability, and Health (ICF) at the level of the medical boards augmented by telemedicine and artificial intelligence (AI). The proposed technologies (Level 1 and Level 2 AI) need to be tried in pilot projects. It will accomplish two goals, the first being the measurement of disability and not merely the impairment. Second, and perhaps more importantly, making the process more transparent in creating a "just" society.

A Review on the Use of Microsoft Kinect for Gait Abnormality and Postural Disorder Assessment

Gait and posture studies have gained much prominence among researchers and have attracted the interest of clinicians. The ability to detect gait abnormality and posture disorder plays a crucial role in the diagnosis and treatment of some diseases. Microsoft Kinect is presented as a noninvasive sensor essential for medical diagnostic and therapeutic purposes. There are currently no relevant studies that attempt to summarise the existing literature on gait and posture abnormalities using Kinect technology. The purpose of this study is to critically evaluate the existing research on gait and posture abnormalities using the Kinect sensor as the main diagnostic tool. Our studies search identified 458 for gait abnormality, 283 for posture disorder of which 26 studies were included for gait abnormality, and 13 for posture. The results indicate that Kinect sensor is a useful tool for the assessment of kinematic features. In conclusion, Microsoft Kinect sensor is presented as a useful tool for gait abnormality, postural disorder analysis, and physiotherapy. It can also help track the progress of patients who are undergoing rehabilitation.

A Deep-Learning Based Posture Detection System for Preventing Telework-Related Musculoskeletal Disorders

The change from face-to-face work to teleworking caused by the pandemic has induced multiple workers to spend more time than usual in front of a computer; in addition, the sudden installation of workstations in homes means that not all of them meet the necessary characteristics for the worker to be able to position himself/herself comfortably with the correct posture in front of their computer. Furthermore, from the point of view of the medical personnel in charge of occupational risk prevention, an automated tool able to quantify the degree of incorrectness of a postural habit in a worker is needed. For this purpose, in this work, a system based on the postural detection of the worker is designed, implemented and tested, using a specialized hardware system that processes video in real time through convolutional neural networks. This system is capable of detecting the posture of the neck, shoulders and arms, providing recommendations to the worker in order to prevent possible health problems, due to poor posture. The results of the proposed system show that this video processing can be carried out in real time (up to 25 processed frames/sec) with a low power consumption (less than 10 watts) using specialized hardware, obtaining an accuracy of over 80% in terms of the pattern detected.

Technology and concussion: A scoping review

Background

Technology for concussion identification and management is rapidly expanding across the continuum of care. Although many technologies offer a range of services around concussion, there is an absence of a non-commercial online location for medical providers to access regarding the functionality of the various technologies used in concussion identification and management.

Objective

The purpose of this review is to present research findings on technology for concussion identification and management.

Methods

Searches for eligible studies were conducted using the PubMed, EMBASE, and Scopus databases with specific search criteria. Through a stepwise process, full-text articles were selected for inclusion if they described clinically useful electronic technologies (i.e. electronics able to be used in standard clinical environments including telehealth) by healthcare providers or end users (i.e. parents or athletes).

Results

A total of 29 articles were included in this review and described technology used to measure symptoms (3), neurocognitive performance (7), the visual system (4), and balance or dual task performance (18). Within the results, various technologies demonstrated increased utility for concussion identification, often detecting subtle deficits not possible with current low-tech clinical methods, differentiating those with concussion from those without concussion, with strong reliability and validity.

Conclusion

Innovative technologies included in this review demonstrate enhanced ability to identify and manage symptoms of concussion, neurocognitive deficits, visual deficits, and balance and dual-task deficits.

Simplified digital balance assessment in typically developing school children

BACKGROUND

Postural balance can be considered a conjoined parameter of gross motor performance. It is acquired in early childhood and honed until adolescence, but may also be influenced by various conditions. A simplified clinical assessment of balance and posture could be helpful in monitoring motor development or therapy particularly in pediatric patients. While analogue scales are considered unprecise and lab-based force-plate posturography lacks accessibility, we propose a novel kinematic balance assessment based on markerless 3D sensor technology.

RESEARCH QUESTION

Can balance and posture be assessed by tracking kinematic data using a single 3D motion tracking camera and are the results representative of normal motor development in a healthy pediatric cohort?

METHODS

A proprietary algorithm was developed and tested that uses skeletal data from the Microsoft Kinect™ V2 3D motion capture camera to calculate and track the center of mass in real time during a set of balance tasks. The algorithm tracks the distance of the COM traveled over time to calculate a balance score (COM speed). For this study, 432 school children aged 4-18 years performed 5 balance tasks and the resulting balance scores were analyzed and correlated with demographic data.

RESULTS

Preliminary experiments demonstrated that the system was able to reliably detect differences in COM speed during different balance tasks. The method showed moderate correlation with age and sex. Athletic activity positively correlated with balance skill in the age group < 8 years, but not in older children. Body mass appeared not to be correlated with balance ability.

SIGNIFICANCE

This study demonstrates that markerless 3D motion analysis can be used for the clinical assessment of coordination and balance and could potentially be used to monitor gross motor performance at the point-of-care.

Level of Agreement Between Human-Rated and Instrumented Balance Error Scoring System Scores

Clinicians have used the Balance Error Scoring System (BESS) to quantify postural control for concussion management. However, the reliability of the human rated BESS has varied prompting the development of instrumented BESSs. A cross-sectional design was used to determine the level of agreement (LOA) between human rated and instrumented BESS scores. Sixty participants completed the BESS on video. An instrumented mat was used to quantify BESS errors while a live human rater simultaneously scored the BESS. A second human rated BESS performance via video. Bland-Altman LOA analyses evaluated agreement between scoring methods (Mat-Human, Mat-Video, Video-Live) for each stance. Mean biases between scores, for each stance, with 95% confidence intervals (95%CIs) were calculated. Agreement between scoring methods was not assessed for the Firm-Double-Limb stance because very few errors were recorded. Agreement between both human raters and the mat was poor based on mean bias estimates > ± 1 and/or wide 95%CIs for all stances including BESS-Total. Agreement between the human raters was better, having displayed consistently smaller mean bias estimates and tighter 95%CIs for all stances and BESS Total. As a result, human rated and instrumented BESS scores may not be comparable. One method should be used to measure BESS errors for consistency.

Feasibility of Home-Based Automated Assessment of Postural Instability and Lower Limb Impairments in Parkinson's Disease

A self-managed, home-based system for the automated assessment of a selected set of Parkinson’s disease motor symptoms is presented. The system makes use of an optical RGB-Depth device both to implement its gesture-based human computer interface and for the characterization and the evaluation of posture and motor tasks, which are specified according to the Unified Parkinson’s Disease Rating Scale (UPDRS). Posture, lower limb movements and postural instability are characterized by kinematic parameters of the patient movement. During an experimental campaign, the performances of patients affected by Parkinson’s disease were simultaneously scored by neurologists and analyzed by the system. The sets of parameters which best correlated with the UPDRS scores of subjects’ performances were then used to train supervised classifiers for the automated assessment of new instances of the tasks. Results on the system usability and the assessment accuracy, as compared to clinical evaluations, indicate that the system is feasible for an objective and automated assessment of Parkinson’s disease at home, and it could be the basis for the development of neuromonitoring and neurorehabilitation applications in a telemedicine framework.

Validity of an Automated Balance Error Scoring System

The Balance Error Scoring System (BESS) is a human-scored, field-based balance test used in cases of suspected concussion. Recently developed instrumented alternatives to human scoring carry substantial advantages over traditional testing, but thus far report relatively abstract outcomes which may not be useful to clinicians or coaches. In contrast, the Automated Assessment of Postural Stability (AAPS) is a computerized system that tabulates error events in accordance with the original description of the BESS. This study compared AAPS and human-based BESS scores. Twenty-five healthy adults performed the modified BESS. Tests were scored twice each by human raters (3) and the computerized system. Interrater (between-human) and inter-method (AAPS vs. human) agreement (ICC_(2,1)) were calculated alongside Bland-Altman limits of agreement (LOA). Interrater analyses were significant (p<0.005) and demonstrated good to excellent agreement. Inter-method agreement analyses were significant (p<0.005), with agreement ranging from poor to excellent. Computerized scores were equivalent across rating occasions. LOA ranges for AAPS vs. the Human Average exceeded the average LOA ranges between human raters. Coaches and clinicians may consider a system such as AAPS to automate balance testing while maintaining the familiarity of human-based scoring, although scores should not yet be considered interchangeable with those of a human rater.

An Objective Balance Error Scoring System for Sideline Concussion Evaluation Using Duplex Kinect Sensors

Sports-related concussion is a common sports injury that might induce potential long-term consequences without early diagnosis and intervention in the field. However, there are few options of such sensor systems available. The aim of the study is to propose and validate an automated concussion administration and scoring approach, which is objective, affordable and capable of detecting all balance errors required by the balance error scoring system (BESS) protocol in the field condition. Our approach is first to capture human body skeleton positions using two Microsoft Kinect sensors in the proposed configuration and merge the data by a custom-made algorithm to remove the self-occlusion of limbs. The standing balance errors according to BESS protocol were further measured and accessed automatically by the proposed algorithm. Simultaneously, the BESS test was filmed for scoring by an experienced rater. Two results were compared using Pearson coefficient r, obtaining an excellent consistency (r = 0.93, p < 0.05). In addition, BESS test–retest was performed after seven days and compared using intraclass correlation coefficients (ICC), showing a good test–retest reliability (ICC = 0.81, p < 0.01). The proposed approach could be an alternative of objective tools to assess postural stability for sideline sports concussion diagnosis.