Preliminary Clinical Application of Textile Insole Sensor for Hemiparetic Gait Pattern Analysis

Smart wearable insoles in industrial environments: A systematic review

BACKGROUND AND PURPOSE

Industrial environments present unique challenges in ensuring worker safety and optimizing productivity. The emergence of smart wearable technologies such as smart insoles has provided new opportunities to address these challenges through accurate unobtrusive monitoring and analysis of workers' activities and physical parameters. This systematic review aims to analyze the utilization of smart wearable insoles in industrial environments, focusing on their applications, employed analysis methods, and potential future directions.

METHODS

A comprehensive review was conducted, involving the analysis of 27 papers that utilized smart wearable insoles in industrial settings. The reviewed articles were evaluated to determine the trends in application and methodology, explore the implementation of smart insoles across different industries, and identify the prevalent machine learning models and analyzed activities in the relevant literature.

RESULTS

The majority of the reviewed articles (67%) primarily focused on human activity recognition and gesture estimation using smart wearable insoles, aiming to enhance safety and productivity in industrial settings. Furthermore, 10% of the studies focused on fatigue identification, 10% on slip, trip, and fall hazard detection, and 13% on biomechanical analyses of workers' body joint loads. The construction industry accounted for approximately 60% of the studies conducted in industrial settings using smart insoles. The most prevalent machine learning models utilized in these studies were neural networks (48%), support vector machines (33%), k-nearest neighbors (30%), decision trees (26%), and random forests (15%). These models achieved median accuracies of 95%, 96%, 91%, 92%, and 95%, respectively. Among the analyzed activities, walking, bending with/without lifting/lowering a load, and carrying a load were the most frequently considered, with frequencies of 10, 10, and 7 out of the 27 studies, respectively.

CONCLUSION

The findings of this systematic review demonstrate the growing interest in implementing smart wearable insoles in industrial environments to enhance safety and productivity. However, the effectiveness of these systems is dependent on factors such as accuracy, reliability, and generalizability of the models. The review highlights the need for further research to address these challenges and to explore the potential of these systems for use in other industrial applications such as manufacturing. Overall, this systematic review provides valuable insights for researchers, practitioners, and policymakers in the field of occupational health and safety.

Can We Target Close Therapeutic Goals in the Gait Re-Education Algorithm for Stroke Patients at the Beginning of the Rehabilitation Process?

(1) Background: The study aimed to determine the most important activities of the knee joints related to gait re-education in patients in the subacute period after a stroke. We focused on the tests that a physiotherapist could perform in daily clinical practice. (2) Methods: Twenty-nine stroke patients (SG) and 29 healthy volunteers (CG) were included in the study. The patients underwent the 5-meter walk test (5mWT) and the Timed Up and Go test (TUG). Tests such as step up, step down, squat, step forward, and joint position sense test (JPS) were also performed, and the subjects were assessed using wireless motion sensors. (3) Results: We observed significant differences in the time needed to complete the 5mWT and TUG tests between groups. The results obtained in the JPS show a significant difference between the paretic and the non-paretic limbs compared to the CG group. A significantly smaller range of knee joint flexion (ROM) was observed in the paretic limb compared to the non-paretic and control limbs in the step down test and between the paretic and non-paretic limbs in the step forward test. (4) Conclusions: The described functional tests are useful in assessing a stroke patient's motor skills and can be performed in daily clinical practice.

Accuracy, concurrent validity, and test-retest reliability of pressure-based insoles for gait measurement in chronic stroke patients

Introduction

Wearables are potentially valuable tools for understanding mobility behavior in individuals with neurological disorders and how it changes depending on health status, such as after rehabilitation. However, the accurate detection of gait events, which are crucial for the evaluation of gait performance and quality, is challenging due to highly individual-specific patterns that also vary greatly in movement and speed, especially after stroke. Therefore, the purpose of this study was to assess the accuracy, concurrent validity, and test-retest reliability of a commercially available insole system in the detection of gait events and the calculation of stance duration in individuals with chronic stroke.

Methods

Pressure insole data were collected from 17 individuals with chronic stroke during two measurement blocks, each comprising three 10-min walking tests conducted in a clinical setting. The gait assessments were recorded with a video camera that served as a ground truth, and pressure insoles as an experimental system. We compared the number of gait events and stance durations between systems.

Results and discussion

Over all 3,820 gait events, 90.86% were correctly identified by the insole system. Recall values ranged from 0.994 to 1, with a precision of 1 for all measurements. The F1 score ranged from 0.997 to 1. Excellent absolute agreement (Intraclass correlation coefficient, ICC = 0.874) was observed for the calculation of the stance duration, with a slightly longer stance duration recorded by the insole system (difference of -0.01 s). Bland-Altmann analysis indicated limits of agreement of 0.33 s that were robust to changes in walking speed. This consistency makes the system well-suited for individuals post-stroke. The test-retest reliability between measurement timepoints T1 and T2 was excellent (ICC = 0.928). The mean difference in stance duration between T1 and T2 was 0.03 s. We conclude that the insole system is valid for use in a clinical setting to quantitatively assess continuous walking in individuals with stroke.

Sensor-Based Wearable Systems for Monitoring Human Motion and Posture: A Review

In recent years, marked progress has been made in wearable technology for human motion and posture recognition in the areas of assisted training, medical health, VR/AR, etc. This paper systematically reviews the status quo of wearable sensing systems for human motion capture and posture recognition from three aspects, which are monitoring indicators, sensors, and system design. In particular, it summarizes the monitoring indicators closely related to human posture changes, such as trunk, joints, and limbs, and analyzes in detail the types, numbers, locations, installation methods, and advantages and disadvantages of sensors in different monitoring systems. Finally, it is concluded that future research in this area will emphasize monitoring accuracy, data security, wearing comfort, and durability. This review provides a reference for the future development of wearable sensing systems for human motion capture.

Ground-Reaction-Force-Based Gait Analysis and Its Application to Gait Disorder Assessment: New Indices for Quantifying Walking Behavior

Gait assessment is an important tool for determining whether a person has a gait disorder. Existing gait analysis studies have a high error rate due to the heel-contact-event-based technique. Our goals were to overcome the shortcomings of existing gait analysis techniques and to develop more objective indices for assessing gait disorders. This paper proposes a method for assessing gait disorders via the observation of changes in the center of pressure (COP) in the medial-lateral direction, i.e., COPx, during the gait cycle. The data for the COPx were used to design a gait cycle estimation method applicable to patients with gait disorders. A polar gaitogram was drawn using the gait cycle and COPx data. The difference between the areas inside the two closed curves in the polar gaitogram, area ratio index (ARI), and the slope of the tangential line common to the two closed curves were proposed as gait analysis indices. An experimental study was conducted to verify that these two indices can be used to differentiate between stroke patients and healthy adults. The findings indicated the potential of using the proposed polar gaitogram and indices to develop and apply wearable devices to assess gait disorders.

Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis

Background: Gait dysfunction or impairment is considered one of the most common and devastating physiological consequences of stroke, and achieving optimal gait is a key goal for stroke victims with gait disability along with their clinical teams. Many researchers have explored post stroke gait, including assessment tools and techniques, key gait parameters and significance on functional recovery, as well as data mining, modeling and analyses methods.

Research Question: This study aimed to review and summarize research efforts applicable to quantification and analyses of post-stroke gait with focus on recent technology-driven gait characterization and analysis approaches, including the integration of smart low cost wearables and Artificial Intelligence (AI), as well as feasibility and potential value in clinical settings.

Methods: A comprehensive literature search was conducted within Google Scholar, PubMed, and ScienceDirect using a set of keywords, including lower extremity, walking, post-stroke, and kinematics. Original articles that met the selection criteria were included.

Results and Significance: This scoping review aimed to shed light on tools and technologies employed in post stroke gait assessment toward bridging the existing gap between the research and clinical communities. Conventional qualitative gait analysis, typically used in clinics is mainly based on observational gait and is hence subjective and largely impacted by the observer's experience. Quantitative gait analysis, however, provides measured parameters, with good accuracy and repeatability for the diagnosis and comparative assessment throughout rehabilitation. Rapidly emerging smart wearable technology and AI, including Machine Learning, Support Vector Machine, and Neural Network approaches, are increasingly commanding greater attention in gait research. Although their use in clinical settings are not yet well leveraged, these tools promise a paradigm shift in stroke gait quantification, as they provide means for acquiring, storing and analyzing multifactorial complex gait data, while capturing its non-linear dynamic variability and offering the invaluable benefits of predictive analytics.

Utilization of wearable technology to assess gait and mobility post-stroke: a systematic review

Background

Extremity weakness, fatigue, and postural instability often contribute to mobility deficits in persons after stroke. Wearable technologies are increasingly being utilized to track many health-related parameters across different patient populations. The purpose of this systematic review was to identify how wearable technologies have been used over the past decade to assess gait and mobility in persons with stroke.

Methods

We performed a systematic search of Ovid MEDLINE, CINAHL, and Cochrane databases using select keywords. We identified a total of 354 articles, and 13 met inclusion/exclusion criteria. Included studies were quality assessed and data extracted included participant demographics, type of wearable technology utilized, gait parameters assessed, and reliability and validity metrics.

Results

The majority of studies were performed in either hospital-based or inpatient settings. Accelerometers, activity monitors, and pressure sensors were the most commonly used wearable technologies to assess gait and mobility post-stroke. Among these devices, spatiotemporal parameters of gait that were most widely assessed were gait speed and cadence, and the most common mobility measures included step count and duration of activity. Only 4 studies reported on wearable technology validity and reliability metrics, with mixed results.

Conclusion

The use of various wearable technologies has enabled researchers and clinicians to monitor patients’ activity in a multitude of settings post-stroke. Using data from wearables may provide clinicians with insights into their patients’ lived-experiences and enrich their evaluations and plans of care. However, more studies are needed to examine the impact of stroke on community mobility and to improve the accuracy of these devices for gait and mobility assessments amongst persons with altered gait post-stroke.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-021-00863-x.

A Soft Wearable and Fully-Textile Piezoresistive Sensor for Plantar Pressure Capturing

The trends of wearable health monitoring systems have led to growing demands for gait-capturing devices. However, comfortability and durability under repeated stress are still challenging to achieve in existing sensor-enabled footwear. Herein, a flexible textile piezoresistive sensor (TPRS) consisting of a reduced graphene oxide (rGO)-cotton) fabric electrode and an Ag fabric circuit electrode is proposed. Based on the mechanical and electrical properties of the two fabric electrodes, the TPRS exhibits superior sensing performance, with a high sensitivity of 3.96 kPa-1 in the lower pressure range of 0-36 kPa, wide force range (0-100 kPa), fast response time (170 ms), remarkable durability stability (1000 cycles) and detection ability in different pressures ranges. For the prac-tical application of capturing plantar pressure, six TPRSs were mounted on a flexible printed circuit board and integrated into an insole. The dynamic plantar pressure distribution during walking was derived in the form of pressure maps. The proposed fully-textile piezoresistive sensor is a strong candidate for next-generation plantar pressure wearable monitoring devices.

Smart Helmet and Insole Sensors for Near Fall Incidence Recognition during Descent of Stairs

Stair falls on construction sites are leading causes of fatal injuries, and the dangers for workers are greater because they usually carry heavy loads. Nevertheless, there are very few studies related to stair falls among construction workers. The purpose of this study was to detect near fall incidence during stair descent and analyze the changes in weight bearing and center of pressure. A total of 10 healthy males participated in this study. Three experimental conditions were set up to analyze stair falls: natural descent (E1), weighted descent (E2), and near fall-simulated descent (E3). While walking down the stairs, subjects wore a three-axis accelerometer sensor attached to a Smart Helmet and a pair of textile pressure sensors (insole) placed inside Smart Shoes. The collected data were analyzed for: (1) whole body balance, (2) plantar pressure distribution, (3) head tilt pattern, and (4) conformity between the helmet and insole sensors. The results showed that our proposed smart helmet and smart shoes have relatively good performance in terms of classifying the weight-shifting patterns in the head and the feet during stair descent. The results of this study may be helpful in detecting near falls of workers on construction sites.

Perspectives on the prospective development of stroke-specific lower extremity wearable monitoring technology: a qualitative focus group study with physical therapists and individuals with stroke

BACKGROUND

Wearable activity monitors that track step count can increase the wearer's physical activity and motivation but are infrequently designed for the slower gait speed and compensatory patterns after stroke. New and available technology may allow for the design of stroke-specific wearable monitoring devices, capable of detecting more than just step counts, which may enhance how rehabilitation is delivered. The objective of this study was to identify important considerations in the development of stroke-specific lower extremity wearable monitoring technology for rehabilitation, from the perspective of physical therapists and individuals with stroke.

METHODS

A qualitative research design with focus groups was used to collect data. Five focus groups were conducted, audio recorded, and transcribed verbatim. Data were analyzed using content analysis to generate overarching categories representing the stakeholder considerations for the development of stroke-specific wearable monitor technology for the lower extremity.

RESULTS

A total of 17 physical therapists took part in four focus group discussions and three individuals with stroke participated in the fifth focus group. Our analysis identified four main categories for consideration: 1) 'Variability' described the heterogeneity of patient presentation, therapy approaches, and therapeutic goals that are taken into account for stroke rehabilitation; 2) 'Context of use' described the different settings and purposes for which stakeholders could foresee employing stroke-specific wearable technology; 3) 'Crucial design features' identified the measures, functions, and device characteristics that should be considered for incorporation into prospective technology to enhance uptake; and 4) 'Barriers to adopting technology' highlighted challenges, including personal attitudes and design flaws, that may limit the integration of current and future wearable monitoring technology into clinical practice.

CONCLUSIONS

The findings from this qualitative study suggest that the development of stroke-specific lower extremity wearable monitoring technology is viewed positively by physical therapists and individuals with stroke. While a single, specific device or function may not accommodate all the variable needs of therapists and their clients, it was agreed that wearable monitoring technology could enhance how physical therapists assess and treat their clients. Future wearable devices should be developed in consideration of the highlighted design features and potential barriers for uptake.