Local Dynamic Stability Assessment of Motion Impaired Elderly Using Electronic Textile Pants

Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter γ—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter γ—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of an SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter γ —are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

Automatic Detection of Dynamic and Static Activities of the Elderly using a Wearable Sensor and Support Vector Machines

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the elderly is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of an SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the elderly. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter —are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying static and dynamic activities of daily life in the elderly.

Assessing the effects of slippery steel beam coatings to ironworkers' gait stability

Since ironworkers walk and perform their tasks on steel beams, identifying the effects of slippery steel beam surfaces on ironworkers' gait stability-which can be related to safety risk-is critical. However, there is no accepted or validated standard for measuring the slipperiness of coated steel beams, which makes evaluating and controlling for slipperiness a challenge. In this context, this study investigated the effect of the slipperiness of steel beam coatings on ironworkers' gait stability. Accordingly, to identify the relationships between coefficient of friction, perceived slipperiness, and gait stability-represented as the Maximum Lyaponuv exponent (Max LE)-an experiment was conducted with eight different surfaces and sixteen subjects with varying experience as ironworkers. The experiment's results indicate that the slipperiness of the various surfaces greatly affect ironworkers' gait stability while they walk on coated steel beam surfaces. In detail, the Max LE of two subject groups-experienced and inexperienced ironworkers-highly correlated with both the dynamic coefficient of friction values measured by following ANSI B101.3 and with the subjective rating scores of the inexperienced subject group. Unlike subjective rating scores-which were particularly incongruent among experienced workers-the Max LE of inexperienced and experienced subjects has a consistent pattern. This study result highlights an opportunity for using gait stability measurements to quantify and differentiate the safety risks caused by slippery coated steel beams in the future.

Feature selection for elderly faller classification based on wearable sensors

BACKGROUND

Wearable sensors can be used to derive numerous gait pattern features for elderly fall risk and faller classification; however, an appropriate feature set is required to avoid high computational costs and the inclusion of irrelevant features. The objectives of this study were to identify and evaluate smaller feature sets for faller classification from large feature sets derived from wearable accelerometer and pressure-sensing insole gait data.

METHODS

A convenience sample of 100 older adults (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, left and right shanks. Feature selection was performed using correlation-based feature selection (CFS), fast correlation based filter (FCBF), and Relief-F algorithms. Faller classification was performed using multi-layer perceptron neural network, naïve Bayesian, and support vector machine classifiers, with 75:25 single stratified holdout and repeated random sampling.

RESULTS

The best performing model was a support vector machine with 78% accuracy, 26% sensitivity, 95% specificity, 0.36 F1 score, and 0.31 MCC and one posterior pelvis accelerometer input feature (left acceleration standard deviation). The second best model achieved better sensitivity (44%) and used a support vector machine with 74% accuracy, 83% specificity, 0.44 F1 score, and 0.29 MCC. This model had ten input features: maximum, mean and standard deviation posterior acceleration; maximum, mean and standard deviation anterior acceleration; mean superior acceleration; and three impulse features. The best multi-sensor model sensitivity (56%) was achieved using posterior pelvis and both shank accelerometers and a naïve Bayesian classifier. The best single-sensor model sensitivity (41%) was achieved using the posterior pelvis accelerometer and a naïve Bayesian classifier.

CONCLUSIONS

Feature selection provided models with smaller feature sets and improved faller classification compared to faller classification without feature selection. CFS and FCBF provided the best feature subset (one posterior pelvis accelerometer feature) for faller classification. However, better sensitivity was achieved by the second best model based on a Relief-F feature subset with three pressure-sensing insole features and seven head accelerometer features. Feature selection should be considered as an important step in faller classification using wearable sensors.

Prospective Fall-Risk Prediction Models for Older Adults Based on Wearable Sensors

Wearable sensors can provide quantitative, gait-based assessments that can translate to point-of-care environments. This investigation generated elderly fall-risk predictive models based on wearable-sensor-derived gait data and prospective fall occurrence, and identified the optimal sensor type, location, and combination for single and dual-task walking. 75 individuals who reported six month prospective fall occurrence (75.2 ± 6.6 years; 47 non-fallers and 28 fallers) walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensinginsoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Fall-risk classificationmodels were assessed for all sensor combinations and three model types: neural network, naïve Bayesian, and support vector machine. The best performing model used a neural network, dual-task gait data, and input parameters from head, pelvis, and left shank accelerometers (accuracy = 57%, sensitivity = 43%, and specificity = 65%). The best single-sensor model used a neural network, dual-task gait data, and pelvis accelerometer parameters (accuracy = 54%, sensitivity = 35%, and specificity = 67%). Single-task and dual-task gait assessments provided similar fall-risk model performance. Fall-risk predictive models developed for point-of-care environments should use multi-sensor dual-task gait assessment with the pelvis location considered if assessment is limited to a single sensor.

Influence of focus of attention, reinvestment and fall history on elderly gait stability

Falls represent a substantial risk in the elderly. Previous studies have found that a focus on the outcome or effect of the movement (external focus of attention) leads to improved balance performance, whereas a focus on the movement execution itself (internal focus of attention) impairs balance performance in elderly. A shift toward more conscious, explicit forms of motor control occurs when existing declarative knowledge is recruited in motor control, a phenomenon called reinvestment. We investigated the effects of attentional focus and reinvestment on gait stability in elderly fallers and nonfallers. Full body kinematics was collected from twenty-eight healthy older adults walking on a treadmill, while focus of attention was manipulated through instruction. Participants also filled out the Movement Specific Reinvestment Scale (MSRS) and the Falls Efficacy Scale International (FES-I), and provided details about their fall history. Coefficients of Variation (CV) of spatiotemporal gait parameters and Local Divergence Exponents (LDE) were calculated as measures of gait variability and gait stability, respectively. Larger stance time CV and LDE (decreased gait stability) were found for fallers compared to nonfallers. No significant effect of attentional focus was found for the gait parameters, and no significant relation between MSRS score (reinvestment) and fall history was found. We conclude that external attention to the walking surface does not lead to improved gait stability in elderly. Potential benefits of an external focus of attention might not apply to gait, because walking movements are not geared toward achieving a distinct environmental effect.

A review of e-textiles in neurological rehabilitation: How close are we?

Textiles able to perform electronic functions are known as e-textiles, and are poised to revolutionise the manner in which rehabilitation and assistive technology is provided. With numerous reports in mainstream media of the possibilities and promise of e-textiles it is timely to review research work in this area related to neurological rehabilitation.This paper provides a review based on a systematic search conducted using EBSCO- Health, Scopus, AMED, PEDro and ProQuest databases, complemented by articles sourced from reference lists. Articles were included if the e-textile technology described had the potential for use in neurological rehabilitation and had been trialled on human participants. A total of 108 records were identified and screened, with 20 meeting the broad review inclusion criteria. Nineteen user trials of healthy people and one pilot study with stroke participants have been reported.The review identifies two areas of research focus; motion sensing, and the measurement of, or stimulation of, muscle activity. In terms of motion sensing, E-textiles appear able to reliably measure gross movement and whether an individual has achieved a predetermined movement pattern. However, the technology still remains somewhat cumbersome and lacking in resolution at present. The measurement of muscle activity and the provision of functional electrical stimulation via e-textiles is in the initial stages of development but shows potential for e-textile expansion into assistive technologies.The review identified a lack of high quality clinical evidence and, in some cases, a lack of practicality for clinical application. These issues may be overcome by engagement of clinicians in e-textile research and using their expertise to develop products that augment and enhance neurological rehabilitation practice.

Wearable-Sensor-Based Classification Models of Faller Status in Older Adults

Wearable sensors have potential for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical-care and older-adult living environments. This investigation generated models for wearable-sensor based fall-risk classification in older adults and identified the optimal sensor type, location, combination, and modelling method; for walking with and without a cognitive load task. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. The best performing model was a multi-layer perceptron neural network with input parameters from pressure-sensing insoles and head, pelvis, and left shank accelerometers (accuracy = 84%, F1 score = 0.600, MCC score = 0.521). Head sensor-based models had the best performance of the single-sensor models for single-task gait assessment. Single-task gait assessment models outperformed models based on dual-task walking or clinical assessment data. Support vector machines and neural networks were the best modelling technique for fall risk classification. Fall risk classification models developed for point-of-care environments should be developed using support vector machines and neural networks, with a multi-sensor single-task gait assessment.

Analysis of dual-task elderly gait in fallers and non-fallers using wearable sensors

Dual-task (DT) gait involves walking while simultaneously performing an attention-demanding task and can be used to identify impaired gait or executive function in older adults. Advancment is needed in techniques that quantify the influence of dual tasking to improve predictive and diagnostic potential. This study investigated the viability of wearable sensor measures to identify DT gait changes in older adults and distinguish between elderly fallers and non-fallers. A convenience sample of 100 older individuals (75.5±6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62m under single-task (ST) and DT conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Differences between ST and DT gait were identified for temporal measures, acceleration descriptive statistics, Fast Fourier Transform (FFT) quartiles, ratio of even to odd harmonics, center of pressure (CoP) stance path coefficient of variation, and deviations to expected CoP stance path. Increased posterior CoP stance path deviations, increased coefficient of variation, decreased FFT quartiles, and decreased ratio of even to odd harmonics suggested increased DT gait variability. Decreased gait velocity and decreased acceleration standard deviations (SD) at the pelvis and shanks could represent compensatory gait strategies that maintain stability. Differences in acceleration between fallers and non-fallers in head posterior SD and pelvis AP ratio of even to odd harmonics during ST, and pelvis vertical maximum Lyapunov exponent during DT gait were identified. Wearable-sensor-based DT gait assessments could be used in point-of-care environments to identify gait deficits.

Traditional posterior load carriage: effects of load mass and size on torso kinematics, kinetics, muscle activity and movement stability

Traditional posterior load carriage (PLC), done without the use of an assistive device (e.g., backpack), has been associated with low back pain (LBP) development. This study evaluated the effects of important task demands, related to load mass and size, on potential mechanisms linking traditional PLC with LBP. Nine healthy participants completed PLC tasks with three load masses (20%, 35% and 50% of individual body mass) and three load sizes (small, medium and large). Torso kinematics, kinetics, muscle activity and slip risk were evaluated during PLC on a walkway, and torso movement stability was quantified during PLC on a treadmill. Increasing load mass caused increased torso flexion, L5/S1 flexion moment, abdominal muscle activity and torso movement stability in the frontal plane. Increasing load size also caused higher torso flexion, peak torso angular velocity and acceleration, and abdominal muscle activity. Complex interactive effects of load mass and size were found on paraspinal muscle activity and slip risk. Specific task demands, related to load mass and size, may thus influence the risk of LBP during PLC.

PRACTITIONER SUMMARY

This study examined the effects of load mass and size on low back pain (LBP) risk using intermediary measures derived from torso kinematics, kinetics and muscle activity. Our current findings, along with earlier work, suggest that load mass and size can influence LBP risk, and that use of smaller and light loads may be beneficial during PLC.

Understanding dynamic stability from pelvis accelerometer data and the relationship to balance and mobility in transtibial amputees

This study investigated whether pelvis acceleration-derived parameters can differentiate between dynamic stability states for transtibial amputees during level (LG) and uneven ground (UG) walking. Correlations between these parameters and clinical balance and mobility measures were also investigated. A convenience sample of eleven individuals with unilateral transtibial amputation walked on LG and simulated UG while pelvis acceleration data were collected at 100Hz. Descriptive statistics, Fast Fourier Transform, ratio of even to odd harmonics, and maximum Lyapunov exponent measures were derived from acceleration data. Of the 26 pelvis acceleration measures, seven had a significant difference (p≤0.05) between LG and UG walking conditions. Seven distinct, stability-relevant measures appeared in at least one of the six regression models that correlated accelerometer-derived measures to Berg Balance Scale (BBS), Community Balance and Mobility Scale (CBMS), and Prosthesis Evaluation Questionnaire (PEQ) scores, explaining up to 100% of the variability in these measures. Of these seven measures, medial-lateral acceleration range was the most frequent model variable, appearing in four models. Anterior-posterior acceleration standard deviation and stride time appeared in three models. Pelvis acceleration-derived parameters were able to differentiate between LG and UG walking for transtibial amputees. UG walking provided the most relevant data for balance and mobility assessment. These results could translate to point of patient contact assessments using a wearable system such as a smartbelt or accelerometer-equipped smartphone.

Analysis of dual-task elderly gait using wearable plantar-pressure insoles and accelerometer

Dual-task gait allows assessment of impaired executive function and mobility control in older individuals, which are risk factors of falls. This study investigated gait changes in older individuals due to the addition of a cognitive load, using wearable pressure-sensing insole and tri-axial accelerometer measures. These wearable sensors can be applied at the point-of-care. Eleven elderly (65 years or older) individuals walked 7.62 m with and without a verbal fluency cognitive load task while wearing FScan 3000E pressure-sensing insoles in both shoes and a Gulf Coast X16-1C tri-axial accelerometer at the pelvis. Plantar-pressure derived parameters included center of force (CoF) path and temporal measures. Acceleration derived measures were descriptive statistics, Fast Fourier Transform quartile, ratio of even-to-odd harmonics, and maximum Lyapunov exponent. Stride time, stance time, and swing time all significantly increased during dual-task compared to single-task walking. Minimum, mean, and median CoF stance velocity; cadence; and vertical, anterior-posterior, and medial-lateral harmonic ratio all significantly decreased during dual-task walking. Wearable plantar pressure-sensing insole and lower back accelerometer derived-measures can identify gait differences between single-task and dual-task walking in older individuals and could be used in point-of-care environments to assess for deficits in executive function and mobility impairments.

Classifying lower extremity muscle fatigue during walking using machine learning and inertial sensors

Fatigue in lower extremity musculature is associated with decline in postural stability, motor performance and alters normal walking patterns in human subjects. Automated recognition of lower extremity muscle fatigue condition may be advantageous in early detection of fall and injury risks. Supervised machine learning methods such as support vector machines (SVMs) have been previously used for classifying healthy and pathological gait patterns and also for separating old and young gait patterns. In this study we explore the classification potential of SVM in recognition of gait patterns utilizing an inertial measurement unit associated with lower extremity muscular fatigue. Both kinematic and kinetic gait patterns of 17 participants (29 ± 11 years) were recorded and analyzed in normal and fatigued state of walking. Lower extremities were fatigued by performance of a squatting exercise until the participants reached 60% of their baseline maximal voluntary exertion level. Feature selection methods were used to classify fatigue and no-fatigue conditions based on temporal and frequency information of the signals. Additionally, influences of three different kernel schemes (i.e., linear, polynomial, and radial basis function) were investigated for SVM classification. The results indicated that lower extremity muscle fatigue condition influenced gait and loading responses. In terms of the SVM classification results, an accuracy of 96% was reached in distinguishing the two gait patterns (fatigue and no-fatigue) within the same subject using the kinematic, time and frequency domain features. It is also found that linear kernel and RBF kernel were equally good to identify intra-individual fatigue characteristics. These results suggest that intra-subject fatigue classification using gait patterns from an inertial sensor holds considerable potential in identifying "at-risk" gait due to muscle fatigue.

Review of fall risk assessment in geriatric populations using inertial sensors

Background

Falls are a prevalent issue in the geriatric population and can result in damaging physical and psychological consequences. Fall risk assessment can provide information to enable appropriate interventions for those at risk of falling. Wearable inertial-sensor-based systems can provide quantitative measures indicative of fall risk in the geriatric population.

Methods

Forty studies that used inertial sensors to evaluate geriatric fall risk were reviewed and pertinent methodological features were extracted; including, sensor placement, derived parameters used to assess fall risk, fall risk classification method, and fall risk classification model outcomes.

Results

Inertial sensors were placed only on the lower back in the majority of papers (65%). One hundred and thirty distinct variables were assessed, which were categorized as position and angle (7.7%), angular velocity (11.5%), linear acceleration (20%), spatial (3.8%), temporal (23.1%), energy (3.8%), frequency (15.4%), and other (14.6%). Fallers were classified using retrospective fall history (30%), prospective fall occurrence (15%), and clinical assessment (32.5%), with 22.5% using a combination of retrospective fall occurrence and clinical assessments. Half of the studies derived models for fall risk prediction, which reached high levels of accuracy (62-100%), specificity (35-100%), and sensitivity (55-99%).

Conclusions

Inertial sensors are promising sensors for fall risk assessment. Future studies should identify fallers using prospective techniques and focus on determining the most promising sensor sites, in conjunction with determination of optimally predictive variables. Further research should also attempt to link predictive variables to specific fall risk factors and investigate disease populations that are at high risk of falls.

Wireless slips and falls prediction system

Accidental slips and falls due to decreased strength and stability are a concern for the elderly. A method to detect and ideally predict these falls can reduce their occurrence and allow these individuals to regain a degree of independence. This paper presents the design and assessment of a wireless, wearable device that continuously samples accelerometer and gyroscope data with a goal to detect and predict falls. Lyapunov-based analyses of these time series data indicate that wearer instability can be detected and predicted in real time, implying the ability to predict impending incidents.

Assessing the stability of human locomotion: a review of current measures

Falling poses a major threat to the steadily growing population of the elderly in modern-day society. A major challenge in the prevention of falls is the identification of individuals who are at risk of falling owing to an unstable gait. At present, several methods are available for estimating gait stability, each with its own advantages and disadvantages. In this paper, we review the currently available measures: the maximum Lyapunov exponent (λS and λL), the maximum Floquet multiplier, variability measures, long-range correlations, extrapolated centre of mass, stabilizing and destabilizing forces, foot placement estimator, gait sensitivity norm and maximum allowable perturbation. We explain what these measures represent and how they are calculated, and we assess their validity, divided up into construct validity, predictive validity in simple models, convergent validity in experimental studies, and predictive validity in observational studies. We conclude that (i) the validity of variability measures and λS is best supported across all levels, (ii) the maximum Floquet multiplier and λL have good construct validity, but negative predictive validity in models, negative convergent validity and (for λL) negative predictive validity in observational studies, (iii) long-range correlations lack construct validity and predictive validity in models and have negative convergent validity, and (iv) measures derived from perturbation experiments have good construct validity, but data are lacking on convergent validity in experimental studies and predictive validity in observational studies. In closing, directions for future research on dynamic gait stability are discussed.

Local dynamic stability associated with load carrying

Objectives

Load carrying tasks are recognized as one of the primary occupational factors leading to slip and fall injuries. Nevertheless, the mechanisms associated with load carrying and walking stability remain illusive. The objective of the current study was to apply local dynamic stability measure in walking while carrying a load, and to investigate the possible adaptive gait stability changes.

Methods

Current study involved 25 young adults in a biomechanics research laboratory. One tri-axial accelerometer was used to measure three-dimensional low back acceleration during continuous treadmill walking. Local dynamic stability was quantified by the maximum Lyapunov exponent (maxLE) from a nonlinear dynamics approach.

Results

Long term maxLE was found to be significant higher under load condition than no-load condition in all three reference axes, indicating the declined local dynamic stability associated with load carrying.

Conclusion

Current study confirmed the sensitivity of local dynamic stability measure in load carrying situation. It was concluded that load carrying tasks were associated with declined local dynamic stability, which may result in increased risk of fall accident. This finding has implications in preventing fall accidents associated with occupational load carrying.

Fall risk assessments based on postural and dynamic stability using inertial measurement unit

Objectives

Slip and fall accidents in the workplace are one of the top causes of work related fatalities and injuries. Previous studies have indicated that fall risk was related to postural and dynamic stability. However, the usage of this theoretical relationship was limited by laboratory based measuring instruments. The current study proposed a new method for stability assessment by use of inertial measurement units (IMUs).

Methods

Accelerations at different body parts were recorded by the IMUs. Postural and local dynamic stability was assessed from these measures and compared with that computed from the traditional method.

Results

The results demonstrated: 1) significant differences between fall prone and healthy groups in IMU assessed dynamic stability; and 2) better power of discrimination with multi stability index assessed by IMUs.

Conclusion

The findings can be utilized in the design of a portable screening or monitoring tool for fall risk assessment in various industrial settings.

Speeding up or slowing down?: Gait adaptations to preserve gait stability in response to balance perturbations

It has frequently been proposed that lowering walking speed is a strategy to enhance gait stability and to decrease the probability of falling. However, previous studies have not been able to establish a clear relation between walking speed and gait stability. We investigated whether people do indeed lower walking speed when gait stability is challenged, and whether this reduces the probability of falling. Nine healthy subjects walked on the Computer Assisted Rehabilitation ENvironment (CAREN) system, while quasi-random medio-lateral translations of the walking surface were imposed at four different intensities. A self-paced treadmill setting allowed subjects to regulate their walking speed throughout the trials. Walking speed, step length, step frequency, step width, local dynamic stability (LDS), and margins of stability (MoS) were measured. Subjects did not change walking speed in response to the balance perturbations (p=0.118), but made shorter, faster, and wider steps (p<0.01) with increasing perturbation intensity. Subjects became locally less stable in response to the perturbations (p<0.01), but increased their MoS in medio-lateral (p<0.01) and backward (p<0.01) direction. In conclusion, not a lower walking speed, but a combination of decreased step length and increased step frequency and step width seems to be the strategy of choice to cope with medio-lateral balance perturbations, which increases MoS and thus decreases the risk of falling.

Fabric opto-electronics enabling healthcare applications; a case study

Textiles are a ubiquitous part of human life. By combining them with electronics to create electronic textile systems, new application fields emerge. In this paper, technology and applications of light-emitting textile systems are presented, with emphasis on the healthcare domain: A fabric substrate is described for electronic textile with robust interwoven connections between the conductive yarns in it. This fabric enables the creation of different forms of comfortable light therapy systems. Specific challenges to enable this use in medical applications are discussed.

A clinical study to assess fall risk using a single waist accelerometer

Falls have various causes and are often associated with mobility impairments. Preventive steps to avoid falls may be initiated, if an increasing fall risk could be detected in time. The objective of this article is to identify an automated sensor-based method to determine fall risk of patients based on objectively measured gait parameters. One hundred fifty-one healthy subjects and 90 subjects at risk of falling were measured during a Timed 'Up & Go' test with a single triaxial acceleration sensor worn on a waist belt. The fall risk was assessed using the STRATIFY score. A decision tree induction algorithm was used to distinguish between subjects with high and low risk using the determined gait parameters. The results of the risk classification produce an overall accuracy of 90.4% in relation to STRATIFY score. The sensitivity amount to 89.4%, the specificity to 91.0% and the reliability parameter kappa equals 0.79. The method presented is able to distinguish between subjects with high and low fall risk. It is unobtrusive and therefore may be applied over extended time periods. A subsequent study is needed to confirm the model's suitability for data recorded in patients' everyday lives.

Differentiating fall-prone and healthy adults using local dynamic stability

Variability in kinematic and spatio-temporal gait parameters has long been equated with stability and used to differentiate fallers from non-fallers. Recently, a mathematically rigorous measure of local dynamic stability has been proposed based on the non-linear dynamics theory to differentiate fallers from non-fallers. This study investigated whether the assessment of local dynamic stability can identify fall-prone elderly individuals who were unable to successfully avoid slip-induced falls. Five healthy young, four healthy elderly and four fall-prone elderly individuals participated in a walking experiment. Local dynamic stability was quantified by the maximum Lyapunov exponent. The fall-prone elderly were found to exhibit significantly lower local dynamic stability (i.e. greater sensitivity to local perturbations), as compared to their healthy counterparts. In addition to providing evidence that the increased falls of the elderly may be due to the inability to attenuate/control stride-to-stride disturbances during locomotion, the current study proposed the opportunity of using local dynamic stability as a potential indicator of risk of falling. Early identification of individuals with a higher risk of falling is important for effective fall prevention. The findings from this study suggest that local dynamic stability may be used as a potential fall predictor to differentiate fall-prone adults.