Inertial Sensor Based Analysis of Lie-to-Stand Transfers in Younger and Older Adults

Patient-centric assessment of rheumatoid arthritis using a smartwatch and bespoke mobile app in a clinical setting

Rheumatoid arthritis (RA) is a fluctuating progressive disease requiring frequent symptom assessment for appropriate management. Continuous tracking using digital technologies may provide greater insights of a patient's experience. This prospective study assessed the feasibility, reliability, and clinical utility of using novel digital technologies to remotely monitor participants with RA. Participants with moderate to severe RA and non-RA controls were monitored continuously for 14 days using an iPhone with an integrated bespoke application and an Apple Watch. Participants completed patient-reported outcome measures and objective guided tests designed to assess disease-related impact on physical function. The study was completed by 28 participants with RA, 28 matched controls, and 2 unmatched controls. Completion rates for all assessments were > 97% and were reproducible over time. Several guided tests distinguished between RA and control cohorts (e.g., mean lie-to-stand time [seconds]: RA: 4.77, control: 3.25; P < 0.001). Participants with RA reporting greater stiffness, pain, and fatigue had worse guided test performances (e.g., wrist movement [P < 0.001] and sit-to-stand transition time [P = 0.009]) compared with those reporting lower stiffness, pain, and fatigue. This study demonstrates that digital technologies can be used in a well-controlled, remote clinical setting to assess the daily impact of RA.

A hybrid linear discriminant analysis and genetic algorithm to create a linear model of aging when performing motor tasks through inertial sensors positioned on the hand and forearm

BACKGROUND

During the aging process, cognitive functions and performance of the muscular and neural system show signs of decline, thus making the elderly more susceptible to disease and death. These alterations, which occur with advanced age, affect functional performance in both the lower and upper members, and consequently human motor functions. Objective measurements are important tools to help understand and characterize the dysfunctions and limitations that occur due to neuromuscular changes related to advancing age. Therefore, the objective of this study is to attest to the difference between groups of young and old individuals through manual movements and whether the combination of features can produce a linear correlation concerning the different age groups.

METHODS

This study counted on 99 participants, these were divided into 8 groups, which were grouped by age. The data collection was performed using inertial sensors (positioned on the back of the hand and on the back of the forearm). Firstly, the participants were divided into groups of young and elderly to verify if the groups could be distinguished through the features alone. Following this, the features were combined using the linear discriminant analysis (LDA), which gave rise to a singular feature called the LDA-value that aided in verifying the correlation between the different age ranges and the LDA-value.

RESULTS

The results demonstrated that 125 features are able to distinguish the difference between the groups of young and elderly individuals. The use of the LDA-value allows for the obtaining of a linear model of the changes that occur with aging in the performance of tasks in line with advancing age, the correlation obtained, using Pearson's coefficient, was 0.86.

CONCLUSION

When we compare only the young and elderly groups, the results indicate that there is a difference in the way tasks are performed between young and elderly individuals. When the 8 groups were analyzed, the linear correlation obtained was strong, with the LDA-value being effective in obtaining a linear correlation of the eight groups, demonstrating that although the features alone do not demonstrate gradual changes as a function of age, their combination established these changes.

Reliability and validity of the supine-to-stand test in people with stroke

OBJECTIVE

To investigate the psychometric properties of the supine-to-stand test in people with stroke.

DESIGN

Cross-sectional design.

SUBJECTS

Fifty-two people with stroke (mean (standard deviation) age 63.13 (6.09) years; time post-stroke 93.13 (61.36) months) and 49 healthy older adults (61.90 (7.29) months).

METHODS

Subjects with stroke were recruited from the community dwelling in Hong Kong and  assessed with the supineto- stand test, Fugl-Meyer Motor Assessment, ankle muscle strength test, Berg Balance Scale, limit of stability test, Timed Up-and-Go Test, Six-Minute Walk Test, Chinese version of Activities-specific Balance Confidence scale, Community Integration Measure (CIM-C), and 12-item Short-Form Health Survey (SF-12) in a university-based rehabilitation laboratory.

RESULTS

The supine-to-stand test completion time demonstrated excellent intra-rater, inter-rater and test-retest reliability (intraclass correlation coefficient 0.946-1.000) for the people with stroke. The completion time was significantly negatively correlated with Berg Balance Scale, Six-Minute Walk Test, limit of stability - maximal excursion, and limit of stability - endpoint excursion results (r = -0.391 to -0.507), whereas it was positively correlated with the Timed Up-and-Go test results (r = 0.461). The optimal cut-off supine-to-stand test completion time of 5.25 s is feasible for a clinical measure to distinguish the performance of people with stroke from healthy older adults (area under the curve = 0.852, sensitivity = 81.1%, specificity = 84.0%).

CONCLUSION

The supine-to-stand test is a reliable, sensitive, specific and easy-to-administer clinical test for assessing the supine-to-stand ability of people with stroke.

Static Balance Digital Endpoints with Mon4t: Smartphone Sensors vs. Force Plate

Static balance tests are conducted in various clinics for diagnosis and treatment adjustment. As a result of population aging, the accessibility of these tests should be increased, in the clinic, and for remote patient examination. A number of publications have already conducted static balance evaluations using the sensors embedded in a smartphone. This study focuses on the applicability of using smartphone-based balance assessment on a large scale while considering ease of use, safety, and reliability. The Mon4t^® app was used to acquire the postural motion using different smartphone devices, different smartphone locations, and various standing postures. The signals derived from the app were compared to the center of pressure displacement derived from a force plate. The results showed moderate to high agreement between the two methods, particularly at the tandem stance (0.69 ≤ r ≤ 0.91). Preliminary data collection was conducted on three healthy participants, followed by 50 additional healthy volunteers, aged 65+. The results demonstrated that the Mon4t app can serve as an accessible and inexpensive static balance assessment tool, both in clinical settings and for remote patient monitoring, which is key for enabling telehealth.

Accurate recognition of lower limb ambulation mode based on surface electromyography and motion data using machine learning

Background and Objective The lower limb activity of recognition of the elderly, the weak, the disabled and the sick is an irreplaceable role in the caring of daily life. The main purpose of this study is to assess the feasibility of using the surface electromyography (sEMG) signal and inertial measurement units (IMUs) data to determine the optimal fusion features and classifier for the study of daily ambulation mode recognition. Methods We have carried out several steps of experiments to obtain and test the optimal combination of the sEMG data and the body motion data at the feature level and the most suitable machine learning classification algorithm. Firstly, the sEMG and IMUs signals of eighteen participants performing four different ambulatory activities have recorded using wearable sensors. Secondly, several features of the sEMG sensors and IMU data were extracted and tested by the Markov Random Field based Fisher-Markov feature selector. Finally, four ML classifiers with several feature combinations were estimated with sensitivity, precision and recognition accurate rate of ambulatory activity classification. Results The results of this work showed that all selected features were significantly statistical difference in four ambulation modes. The principal component analysis was used to reduce the dimension of selected sEMG features and IMU features to form a fusion feature input support vector machine classifier, which could predict ambulatory activities with good classification performance. Conclusions It is concluded that the results demonstrate the feasibility of the ML classification model, which could provide a more novel way to guarantee the recognition rate and effectiveness of monitor daily ambulatory activity.

Assessment in the Supine-To-Stand Task and Functional Health from Youth to Old Age: A Systematic Review

Performance in the supine-to-stand (STS) task is an important functional and health marker throughout life, but the evaluation methods and some correlates can impact it. This article aims to examine the studies that assessed the performance of the STS task of young people, adults and the elderly. Evidence of the association between the STS task and body weight status, musculoskeletal fitness and physical activity was investigated, and a general protocol was proposed. MEDLINE/Pubmed and Web of Science databases were accessed for searching studies measuring the STS task directly; identification, objective, design, sample, protocols and results data were extracted; the risk of bias was assessed (PROSPERO CRD42017055693). From 13,155 studies, 37 were included, and all demonstrated a low to moderate risk of bias. The STS task was applied in all world, but the protocols varied across studies, and they lacked detail; robust evidence demonstrating the association between STS task and musculoskeletal fitness was found; there was limited research examining body weight status, physical activity and the STS task performance. In conclusion, the STS task seems to be a universal tool to track motor functional competence and musculoskeletal fitness throughout life for clinical or research purposes.

Impact of the backward chaining method on physical and psychological outcome measures in older adults at risk of falling: a systematic review

BACKGROUND

Being unable to "get up from the floor" is a risk factor and predictor of serious fall-related injuries in older age; however, floor-rise training (FRT) is not widely used. The backward chaining method (BCM) is a success-oriented, step-by-step form of FRT. This systematic review aimed to evaluate the impact of BCM on physical and psychological outcome measures, and its clinical application.

METHODS

Studies were identified through systematic searching of five databases. Criteria for inclusion were: use of BCM as a treatment method, outcome measures related to falls, and participants aged 60 + years. Study quality was evaluated using the Mixed Methods Appraisal Tool and PEDro scale, if applicable.

RESULTS

Seven studies with a total of 446 participants (mean age 82.4 ± 5.3 years) were identified. Emerging evidence shows that BCM significantly improves the ability to get up unassisted from the floor, as well as mobility with reduced fall incidence in older people. Furthermore, it can potentially reduce fear of falling. Reporting on feasibility and acceptance of BCM was limited. Study quality varied widely.

CONCLUSIONS

BCM provides a promising intervention in fall-related recovery strategies for older adults and is most effective when offered to older adults at risk of falling. Considering the small number of included studies and the varying methodological quality, these findings should be evaluated accordingly. The growing evidence regarding the benefits of BCM, yet the lack of adoption into standard care, highlights the need for further research and clinical application of this intervention approach.

Inertial Sensor-Based Motion Analysis of Lower Limbs for Rehabilitation Treatments

The hemiplegic rehabilitation state diagnosing performed by therapists can be biased due to their subjective experience, which may deteriorate the rehabilitation effect. In order to improve this situation, a quantitative evaluation is proposed. Though many motion analysis systems are available, they are too complicated for practical application by therapists. In this paper, a method for detecting the motion of human lower limbs including all degrees of freedom (DOFs) via the inertial sensors is proposed, which permits analyzing the patient's motion ability. This method is applicable to arbitrary walking directions and tracks of persons under study, and its results are unbiased, as compared to therapist qualitative estimations. Using the simplified mathematical model of a human body, the rotation angles for each lower limb joint are calculated from the input signals acquired by the inertial sensors. Finally, the rotation angle versus joint displacement curves are constructed, and the estimated values of joint motion angle and motion ability are obtained. The experimental verification of the proposed motion detection and analysis method was performed, which proved that it can efficiently detect the differences between motion behaviors of disabled and healthy persons and provide a reliable quantitative evaluation of the rehabilitation state.