Application of smart bracelet to monitor frailty-related gait parameters of older Chinese adults: A preliminary study

The gait parameters in patients with Parkinson's Disease under STN-DBS therapy and associated clinical features

OBJECTIVE

We aimed to investigate the gait parameters in patients with subthalamic nucleus deep brain stimulation (STN-DBS) therapy using quantitative gait analyses and reveal the associated clinical features.

METHODS

Parkinson's disease (PD) subjects with STN-DBS who applied to our movement disorders outpatient clinics between December/2021 and March/2022 were enrolled. In addition to the evaluation of the demographic data and the clinical features; clinical scales measuring the freezing of gait (FOG), falls and quality of life were performed. A gait analyzer program was used to perform gait analysis.

RESULTS

Thirty patients with a mean age of 59.4 ± 8.3 (F/M = 7/23) were enrolled. The comparative analyses between the tremor-dominant and akinetic-rigid (AR) subtype patients showed that the step time asymmetry measures were higher in the AR group. The comparative analyses according to the symptom onset side showed that the step length was smaller in those with left-side symptom onset. The correlation analyses showed that there were correlations between the quality-of-life indexes and FOG questionnaire and falls efficacy scale (FES) scores. Finally, the correlation analyses between clinical scales and gait parameters revealed that there were significant correlations between the FES scores and the step length asymmetry (SLA).

CONCLUSION

We found a strong relationship between falls and quality of life indexes of our patients under STN-DBS therapy. In this patient group, particular evaluation of fallings and the follow-up of SLA in gait analysis may constitute important points during the evaluation of patients in routine clinical practice.

Investigation of the gait parameters after donepezil treatment in patients with alzheimer' s disease

INTRODUCTION

Clinical studies remark that gait disturbance is common in patients with Alzheimer's disease (AD). However, the treatment response of gait disturbance in those patients may constitute a more interesting issue that is rarely addressed.

METHODS

In this prospective, interventional study, we included 14 consecutive patients with a new diagnosis of AD. Basal gait analysis was performed using a 'Gait Analyzer' program that was uploaded to a mobile phone. The gait parameters including Step time (ST), step length (SL), step number (SN), gait velocity (GV), and cadence were measured. Afterward, donepezil 5 mg daily was initiated, and the re-assessments were re-performed 8 weeks after the treatment.

RESULTS

The mean age was 71.78 ± 5.02 (F/M = 8/6). The evaluations after the donepezil treatment showed that there was a significant improvement in the SN (p = 0.021) and SL (p = 0.001) in comparison to the basal evaluations. The repeated analysis in the subgroup of early-stage AD subjects (n = 10) yielded that there were significant improvements in SN (p = 0.003), SL (p = 0.005), and cadence (p = 0.026) after treatment.

CONCLUSIONS

Our results support the efficiency of cholinergic treatment in gait functions in AD subjects.

Stable Sparse Classifiers predict cognitive impairment from gait patterns

Background

Although gait patterns disturbances are known to be related to cognitive decline, there is no consensus on the possibility of predicting one from the other. It is necessary to find the optimal gait features, experimental protocols, and computational algorithms to achieve this purpose.

Purposes

To assess the efficacy of the Stable Sparse Classifiers procedure (SSC) for discriminating young and healthy older adults (YA vs. HE), as well as healthy and cognitively impaired elderly groups (HE vs. MCI-E) from their gait patterns. To identify the walking tasks or combinations of tasks and specific spatio-temporal gait features (STGF) that allow the best prediction with SSC.

Methods

A sample of 125 participants (40 young- and 85 older-adults) was studied. They underwent assessment with five neuropsychological tests that explore different cognitive domains. A summarized cognitive index (MDCog), based on the Mahalanobis distance from normative data, was calculated. The sample was divided into three groups (young adults, healthy and cognitively impaired elderly adults) using k-means clustering of MDCog in addition to Age. The participants executed four walking tasks (normal, fast, easy- and hard-dual tasks) and their gait patterns, measured with a body-fixed Inertial Measurement Unit, were used to calculate 16 STGF and dual-task costs. SSC was then employed to predict which group the participants belonged to. The classification's performance was assessed using the area under the receiver operating curves (AUC) and the stable biomarkers were identified.

Results

The discrimination HE vs. MCI-E revealed that the combination of the easy dual-task and the fast walking task had the best prediction performance (AUC = 0.86, sensitivity: 90.1%, specificity: 96.9%, accuracy: 95.8%). The features related to gait variability and to the amplitude of vertical acceleration had the largest predictive power. SSC prediction accuracy was better than the accuracies obtained with linear discriminant analysis and support vector machine classifiers.

Conclusions

The study corroborated that the changes in gait patterns can be used to discriminate between young and healthy older adults and more importantly between healthy and cognitively impaired adults. A subset of gait tasks and STGF optimal for achieving this goal with SSC were identified, with the latter method superior to other classification techniques.

Smart Phone-Based Motion Capture and Analysis: Importance of Operating Envelope Definition and Application to Clinical Use

Human movement is vital for life, with active engagement affording function, limiting disease, and improving quality; with loss resulting in disability; and the treatment and training leading to restoration and enhancement. To foster these endeavors a need exists for a simple and reliable method for the quantitation of movement, favorable for widespread user availability. We developed a Mobile Motion Capture system (MO2CA) employing a smart-phone and colored markers (2, 5, 10 mm) and here define its operating envelope in terms of: (1) the functional distance of marker detection (range), (2) the inter-target resolution and discrimination, (3) the mobile target detection, and (4) the impact of ambient illumination intensity. MO2CA was able to detect and discriminate: (1) single targets over a range of 1 to 18 ft, (2) multiple targets from 1 ft to 11 ft, with inter-target discrimination improving with an increasing target size, (3) moving targets, with minimal errors from 2 ft to 8 ft, and (4) targets within 1 to 18 ft, with an illumination of 100–300 lux. We then evaluated the utility of motion capture in quantitating regional-finger abduction/adduction and whole body–lateral flex motion, demonstrating a quantitative discrimination between normal and abnormal motion. Overall, our results demonstrate that MO2CA has a wide operating envelope with utility for the detection of human movements large and small, encompassing the whole body, body region, and extremity and digit movements. The definition of the effective operating envelope and utility of smart phone-based motion capture as described herein will afford accuracy and appropriate use for future application studies and serve as a general approach for defining the operational bounds of future video capture technologies that arise for potential clinical use.

System Design and Simulation for Square Dance Movement Monitoring Based on Machine Learning

Since the reform and opening, China’s economy has grown rapidly, and people’s living standards have improved significantly. As one of the most effective ways to implement national fitness, square dance has gradually become the main lifestyle of urban communities, an important part of China’s sports construction, and an important indicator to reflect the fitness of the masses and the construction of a well-off society in an all-round way. On the other hand, with the rapid development of internet of things technology, many people can use intelligent bracelets based on machine learning technology to realize motion detection. This technology is also applicable in square dance, which is of great significance to exercise and protect health. This paper first reviews the research status of the internet of things communication protocol and cloud platform, then introduces and analyzes the MQTT communication protocol and Netty high-performance network framework, and studies the integration technology of the internet of things and machine learning. Then, according to the characteristics of the internet of things, a scheme to realize data preprocessing is proposed. The value to be completed is calculated based on the correlation of other attributes corresponding to the k-nearest neighbor model (KNN) and the regression model. Finally, the machine learning algorithm is used to train the results of the three models to obtain the final filling value. The whole scheme design allows the machine learning algorithm to obtain relatively high-quality data in the internal environment. This paper designs a sports monitoring data system for square dance by combining machine learning and internet of things technology, so as to promote national fitness.

Fatigue Assessment of Sedentary Office Workers using Smart Phones: A Preliminary Study

BACKGROUND

Smartphone-based gait assessment provides a novel method to evaluate fatigue.

OBJECTIVE

This study aimed to examine self-reported fatigue and gait parameters recorded using a smartphone before and after an 8-hour work day in bank workers, and identify the relationship between self-reported fatigue and gait parameters.

METHODS

100 bank workers (20-45 years) were tested before and after an 8-hour work day using a reaction time test, self-reported fatigue scale, and gait test. Spearman correlation coefficient analysis and partial least squares regression were used to identify the relationship between self-reported fatigue and gait parameters.

RESULTS

Reaction time and self-reported fatigue increased significantly after work. Gait parameters (step frequency, minimum acceleration, acceleration root mean square, step regularity, and step counts) decreased; step time and step time variability increased significantly (p < 0.05). We found a significant correlation between Δwork engagement (delta: Δ) and Δstep frequency (r = -0.20, p < 0.05), Δwork engagement and Δstep time (r = 0.21, p < 0.05), and Δwork tasks and Δstep symmetry (r = -0.20, p < 0.05).

DISCUSSION

This study suggests that step frequency, step time and step symmetry measured using a smartphone have the potential to be used as predictors of work fatigue.

Impact of walking states, self-reported daily walking amount and age on the gait of older adults measured with a smart-phone app: a pilot study

BACKGROUND

Smartphones provide a cost-effective avenue for gait assessment among older adults in the community. The purpose of this study is to explore the impact of walking state, self-reported daily walking amount, and age on gait quality, using a smartphone application.

METHODS

One hundred older adult individuals from North China, aged 73.0 ± 7.7 years, voluntarily participated in this study. They performed three walking tests: normal walking, fast walking, and visually impaired walking. Three-dimensional acceleration data for gait were obtained using the smartphone app Pocket Gait. This study used multivariate analysis of variance (MANOVA) to explore the effects of the walking state, self-reported daily walking amount, and age on the step frequency, root mean square (RMS) acceleration, step time variability, regularity, and symmetry.

RESULTS

The walking state, self-reported daily walking amount, and age had statistically significant effects on gait quality. Compared with normal walking, the step frequency, RMS acceleration, variability, and regularity were greater in the fast-walking state, and simulated visually impaired walking did not significantly affect gait quality. Relatively older individuals had a significant decline in gait quality compared to (relatively) younger older adult individuals. Compared with older adults who walked less than 1 km a day, older adults who walked more had better gait quality.

CONCLUSIONS

The walking state, self-reported daily walking amount, and age have a significant effect on the gait quality of older adults. Walking with pigmented sunglasses can be used as a training intervention to improve gait performance. Older adult people who walk less than 1 km/day have worse gait quality compared with their counterparts.

Wearable inertial sensors for human movement analysis: a five-year update

INTRODUCTION

The aim of the present review is to track the evolution of wearable IMUs from their use in supervised laboratory- and ambulatory-based settings to their application for long-term monitoring of human movement in unsupervised naturalistic settings.

AREAS COVERED

Four main emerging areas of application were identified and synthesized, namely, mobile health solutions (specifically, for the assessment of frailty, risk of falls, chronic neurological diseases, and for the monitoring and promotion of active living), occupational ergonomics, rehabilitation and telerehabilitation, and cognitive assessment. Findings from recent scientific literature in each of these areas was synthesized from an applied and/or clinical perspective with the purpose of providing clinical researchers and practitioners with practical guidance on contemporary uses of inertial sensors in applied clinical settings.

EXPERT OPINION

IMU-based wearable devices have undergone a rapid transition from use in laboratory-based clinical practice to unsupervised, applied settings. Successful use of wearable inertial sensing for assessing mobility, motor performance and movement disorders in applied settings will rely also on machine learning algorithms for managing the vast amounts of data generated by these sensors for extracting information that is both clinically relevant and interpretable by practitioners.

Smart Bracelet System for Temperature Monitoring and Movement Tracking Analysis

In several epidemic diseases, one of the main symptoms exhibited by people is abnormal body temperature. Therefore, monitoring body temperature is crucial for preventing the spread of infectious diseases and facilitating timely responses. This study presents a wearable bracelet that can be used as a temperature monitoring and trajectory analysis system. The temperature sensor in the bracelet can effectively monitor the body temperature of the wearer in the target scene, and the data transmission between the bracelet and the data collector can effectively detect the movement range and trajectory of the wearer. Through these, the whole set can also detect the direct and indirect contact of the wearer in any period of time, which is very helpful for the prevention and control of infectious diseases and the isolation of potentially infected persons.

How wearable sensors have been utilised to evaluate frailty in older adults: a systematic review

BACKGROUND

Globally the population of older adults is increasing. It is estimated that by 2050 the number of adults over the age of 60 will represent over 21% of the world's population. Frailty is a clinical condition associated with ageing resulting in an increase in adverse outcomes. It is considered the greatest challenge facing an ageing population affecting an estimated 16% of community-dwelling populations worldwide.

AIM

The aim of this systematic review is to explore how wearable sensors have been used to assess frailty in older adults.

METHOD

Electronic databases Medline, Science Direct, Scopus, and CINAHL were systematically searched March 2020 and November 2020. A search constraint of articles published in English, between January 2010 and November 2020 was applied. Papers included were primary observational studies involving; older adults aged > 60 years, used a wearable sensor to provide quantitative measurements of physical activity (PA) or mobility and a measure of frailty. Studies were excluded if they used non-wearable sensors for outcome measurement or outlined an algorithm or application development exclusively. The methodological quality of the selected studies was assessed using the Appraisal Tool for Cross-sectional Studies (AXIS).

RESULTS

Twenty-nine studies examining the use of wearable sensors to assess and discriminate between stages of frailty in older adults were included. Thirteen different body-worn sensors were used in eight different body-locations. Participants were community-dwelling older adults. Studies were performed in home, laboratory or hospital settings. Postural transitions, number of steps, percentage of time in PA and intensity of PA together were the most frequently measured parameters followed closely by gait speed. All but one study demonstrated an association between PA and level of frailty. All reports of gait speed indicate correlation with frailty.

CONCLUSIONS

Wearable sensors have been successfully used to evaluate frailty in older adults. Further research is needed to identify a feasible, user-friendly device and body-location that can be used to identify signs of pre-frailty in community-dwelling older adults. This would facilitate early identification and targeted intervention to reduce the burden of frailty in an ageing population.

How gait influences frailty models and health-related outcomes in clinical-based and population-based studies: a systematic review

Aging is often associated with a decline in physical function that eventually leads to loss of autonomy in activities of daily living (ADL). Walking is a very common ADL, important for main determinants of quality of life in older age, and it requires the integration of many physiological systems. Gait speed has been described as the 'sixth vital sign' because it is a core indicator of health and function in aging and disease. We reviewed original studies up to June 2020 that assessed frailty in both longitudinal and cross-sectional observational studies, paying particular attention to how gait is measured in older population and how the gait parameter adopted may influence the estimated frailty models and the health-related outcomes of the various studies (i.e. clinical, cognitive, physical, and nutritional outcomes). Eighty-five studies met the search strategy and were included in the present systematic review. According to the frailty tools, more than 60% of the studies used the physical phenotype model proposed by Fried and colleagues, while one-third referred to multi-domain indexes or models and only 5% referred to other single-domain frailty models (social or cognitive). The great heterogeneity observed in gait measurements and protocols limited the possibility to directly compare the results of the studies and it could represent an important issue causing variability in the different outcome measures in both clinical-and population-based settings. Gait appeared to be an indicator of health and function also in frail older adults, and different gait parameters appeared to predict adverse health-related outcomes in clinical, cognitive, and physical domains and, to a lesser extent, in nutritional domain. Gait has the potential to elucidate the common basic mechanisms of cognitive and motor decline. Advances in technology may extend the validity of gait in different clinical settings also in frail older adults, and technology-based assessment should be encouraged. Combining various gait parameters may enhance frailty prediction and classification of different frailty phenotypes.

The Effects of a Multicomponent Lower Extremity Training Technique on Physical Function in Healthy Older Adults: A Randomized Controlled Trial

Objective: We aimed to examine the outcomes of our novel multicomponent lower extremity training (MLT) technique on physical function in older adults. Methods: Participants were randomly divided into a training group (TG) or a control group (CG). The TG (4 men, 14 women) received MLT for 24 weeks, once per week. MLT contains strength, balance, and flexibility components. The CG (5 men, 10 women) did not receive any training for 24 weeks. Nine lower extremity range of motions (ROMs; hip flexion, hip abduction, hip adduction, hip extension, internal and external hip rotations, knee flexion, ankle dorsiflexion, and ankle plantar flexion) and two muscle strength assessments (knee extension and flexion) were collected. Physical performance tests were also performed, including the functional reach test, timed up and go test (TUGT), and five times sit-to-stand test (FTSST). Results: After 24 weeks, significant increases were observed in the TG in all ROMs (with the exception of knee flexion), knee extension strength, and performance in the TUGT and FTSST. Conclusions: MLT significantly improved ROM, muscle strength, and physical performance in healthy older adults. We suggest that it is an efficacious intervention in the maintenance and improvement of mobility and functional independence in healthy older adults. Trial registration: UMIN CTR, UMIN000037463. https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000041955.

Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment

BACKGROUND

Frailty is a highly recognized geriatric syndrome resulting in decline in reserve across multiple physiological systems. Impaired physical function is one of the major indicators of frailty. The goal of this study was to evaluate an algorithm that discriminates between frailty groups (non-frail and pre-frail/frail) based on gait performance parameters derived from unsupervised daily physical activity (DPA).

METHODS

DPA was acquired for 48 h from older adults (≥65 years) using a tri-axial accelerometer motion-sensor. Continuous bouts of walking for 20s, 30s, 40s, 50s and 60s without pauses were identified from acceleration data. These were then used to extract qualitative measures (gait variability, gait asymmetry, and gait irregularity) and quantitative measures (total continuous walking duration and maximum number of continuous steps) to characterize gait performance. Association between frailty and gait performance parameters was assessed using multinomial logistic models with frailty as the dependent variable, and gait performance parameters along with demographic parameters as independent variables.

RESULTS

One hundred twenty-six older adults (44 non-frail, 60 pre-frail, and 22 frail, based on the Fried index) were recruited. Step- and stride-times, frequency domain gait variability, and continuous walking quantitative measures were significantly different between non-frail and pre-frail/frail groups (p < 0.05). Among the five different durations (20s, 30s, 40s, 50s and 60s), gait performance parameters extracted from 60s continuous walks provided the best frailty assessment results. Using the 60s gait performance parameters in the logistic model, pre-frail/frail group (vs. non-frail) was identified with 76.8% sensitivity and 80% specificity.

DISCUSSION

Everyday walking characteristics were found to be associated with frailty. Along with quantitative measures of physical activity, qualitative measures are critical elements representing the early stages of frailty. In-home gait assessment offers an opportunity to screen for and monitor frailty.

TRIAL REGISTRATION

The clinical trial was retrospectively registered on June 18th, 2013 with ClinicalTrials.gov, identifier NCT01880229.

A Mobile Phone-Based Gait Assessment App for the Elderly: Development and Evaluation

Background

Gait disorders are common among older adults. With an increase in the use of technology among older adults, a mobile phone app provides a solution for older adults to self-monitor their gait quality in daily life.

Objective

This study aimed to develop a gait-monitoring mobile phone app (Pocket Gait) and evaluate its acceptability and usability among potential older users.

Methods

The app was developed to allow older adults to track their gait quality, including step frequency, acceleration root mean square (RMS), step regularity, step symmetry, and step variability. We recruited a total of 148 community-dwelling older adults aged 60 years and older from two cities in China: Beijing and Chongqing. They walked in three ways (single task, dual task, and fast walking) using a smartphone with the gait-monitoring app installed and completed an acceptability and usability survey after the walk test. User acceptability was measured by a questionnaire including four quantitative measures: perceived ease of use, perceived usefulness, ease of learning, and intention to use. Usability was measured using the System Usability Scale (SUS). Interviews were conducted with participants to collect open-ended feedback questions.

Results

Task type had a significant effect on all gait parameters, namely, step frequency, RMS, step variability, step regularity, and step symmetry (all P values <.001). Age had a significant effect on step frequency (P=.01), and region had a significant effect on step regularity (P=.04). The acceptability of the gait-monitoring app was positive among older adults. Participants identified the usability of the system with an overall score of 59.7 (SD 10.7) out of 100. Older adults from Beijing scored significantly higher SUS compared with older adults from Chongqing (P<.001). The age of older adults was significantly associated with their SUS score (P=.048). Older adults identified improvements such as a larger font size, inclusion of reference values for gait parameters, and inclusion of heart rate and blood pressure monitoring.

Conclusions

This mobile phone app is a health management tool for older adults to self-manage their gait quality and prevent adverse outcomes. In the future, it will be important to take factors such as age and region into consideration while designing a mobile phone–based gait assessment app. The feedback of the participants would help to design more elderly-friendly products.

Are cost-effective technologies feasible to measure gait in older adults? A systematic review of evidence-based literature

BACKGROUND

Unrestricted by time and place, innovative technologies seem to provide cost-effective solutions for gait assessment in older adults.

OBJECTIVE

The objective of this study is to provide an overview of gait assessment for older adults by investigating critical gait characteristics of older adults, discussing advantages and disadvantages of the current gait assessment technologies, as well as device applicability.

METHODS

The Preferred Reporting Item for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were followed during the review. Inclusion criteria were: (1) Sample consisting of adults older than 60 years; (2) qualitative, quantitative, or mixed-method researches using one or more specific gait assessment technologies; and (3) publication in English between 2000 and 2018.

RESULTS

In total, twenty-one studies were included. Gait speed, stride length, frequency, acceleration root mean square, step-to-step consistency, autocorrelation, harmonic ratio were reported in the existing literatures to be associated with falls. The enrolled studies address the use of pedometer, wearable accelerometer-based devices, Kinect, Nintendo Wii Balance Board as cost-effective gait assessment technologies.

CONCLUSIONS

Gait parameters and assessment approaches for older adults are diverse. Cost-effective technologies such as a wearable accelerometer-based device, Kinect, and the Nintendo Wii Balance Board provide potential alternatives for gait assessment with acceptable validity and reliability compared with sophisticated devices. The popularity and development of cost-effective devices have made large-scale data collection for gait assessment possible in the daily environment. Further study could involve older adults and their family members/caregivers in use of these technologies to design elderly-friendly products.

Self-Disclosure to an IoT Conversational Agent: Effects of Space and User Context on Users’ Willingness to Self-Disclose Personal Information

In the era of the Internet of Things (IoT), IoT conversational agents (IoT-CAs) have become the gateways for smart spaces. Users will inevitably self-disclose some types of personal information while interacting with IoT-CAs. In this study, users’ willingness to disclose different types of information to IoT-CAs in two smart spaces (living space and workspace) and two user contexts (one user or two users) was investigated. One living space and one workspace were built for users to experience interactions with IoT-CAs. Subsequently, users’ willingness to self-disclose six types of personal information was measured. Two experiments were separately conducted for a single user (N = 36) and two users (N = 48). The results indicated that users were most willing to disclose information about their tastes and interests and least willing to disclose money information. Users in the living space were willing to disclose more information than those in the workspace, which was mediated by users’ expectations for the reciprocal services of IoT-CAs rather than the awareness of other persons or external factors. Participants had a high private self-awareness in the living space and workspace; their attention was focused on themselves rather than on external factors in smart spaces.