Classifying Step and Spin Turns Using Wireless Gyroscopes and Implications for Fall Risk Assessments

abstract: Recent studies have reported a greater prevalence of spin turns, which are more unstable than step turns, in older adults compared to young adults in laboratory settings. Currently, turning strategies can only be identified through visual observation, either in-person or through video. This paper presents two unique methods and their combination to remotely monitor turning behavior using three uniaxial gyroscopes. Five young adults performed 90° turns at slow, normal, and fast walking speeds around a variety of obstacles while instrumented with three IMUs (attached on the trunk, left and right shank). Raw data from 360 trials were analyzed. Compared to visual classification, the two IMU methods’ sensitivity/specificity to detecting spin turns were 76.1%/76.7% and 76.1%/84.4%, respectively. When the two methods were combined, the IMU had an overall 86.8% sensitivity and 92.2% specificity, with 89.4%/100% sensitivity/specificity at slow speeds. This combined method can be implemented into wireless fall prevention systems and used to identify increased use of spin turns. This method allows for longitudinal monitoring of turning strategies and allows researchers to test for potential associations between the frequency of spin turns and clinically relevant outcomes (e.g., falls) in non-laboratory settings

Effects of Obesity and Fall Risk on Gait and Posture of Community-Dwelling Older Adults

Epidemiological studies link increased fall risk to obesity in older adults, but the mechanism through which obesity increases falls and fall risks is unknown. This study investigates if obesity (Body Mass Index: BMI\u3e30 kg/m2) influenced gait and standing postural characteristics of community dwelling older adults leading to increased risk of falls. One hundred healthy older adults (age 74.0±7.6 years, range of 56-90 years) living independently in a community participated in this study. Participants’ history of falls over the previous two years was recorded, with emphasis on frequency and characteristics of falls. Participants with at least two falls in the prior year were classified as fallers. Each individual was assessed for postural stability during quiet stance and gait stability during 10 meters walking. Fall risk parameters of postural sway (COP area, velocity, path-length) were measured utilizing a standard forceplate coupled with an accelerometer affixed at the sternum. Additionally, parameters of gait stability (walking velocity, double support time, and double support time variability) were assessed utilizing an accelerometer affixed at the participant’s sternum. Gait and postural stability analyses indicate that obese older adults who fell have significantly altered gait pattern (longer double support time and greater variability) exhibiting a loss of automaticity in walking and, postural instability as compared to their counterparts (i.e., higher sway area and path length, and higher sway velocity) further increasing the risk of a fall given a perturbation. Body weight/BMI is a risk factor for falls in older adults as measured by gait and postural stability parameters

Classifying Step and Spin Turns Using Wireless Gyroscopes and Implications for Fall Risk Assessments

Recent studies have reported a greater prevalence of spin turns, which are more unstable than step turns, in older adults compared to young adults in laboratory settings. Currently, turning strategies can only be identified through visual observation, either in-person or through video. This paper presents two unique methods and their combination to remotely monitor turning behavior using three uniaxial gyroscopes. Five young adults performed 90° turns at slow, normal, and fast walking speeds around a variety of obstacles while instrumented with three IMUs (attached on the trunk, left and right shank). Raw data from 360 trials were analyzed. Compared to visual classification, the two IMU methods’ sensitivity/specificity to detecting spin turns were 76.1%/76.7% and 76.1%/84.4%, respectively. When the two methods were combined, the IMU had an overall 86.8% sensitivity and 92.2% specificity, with 89.4%/100% sensitivity/specificity at slow speeds. This combined method can be implemented into wireless fall prevention systems and used to identify increased use of spin turns. This method allows for longitudinal monitoring of turning strategies and allows researchers to test for potential associations between the frequency of spin turns and clinically relevant outcomes (e.g., falls) in non-laboratory settings

Classifying Step and Spin Turns Using Wireless Gyroscopes and Implications for Fall Risk Assessments

Recent studies have reported a greater prevalence of spin turns, which are more unstable than step turns, in older adults compared to young adults in laboratory settings. Currently, turning strategies can only be identified through visual observation, either in-person or through video. This paper presents two unique methods and their combination to remotely monitor turning behavior using three uniaxial gyroscopes. Five young adults performed 90° turns at slow, normal, and fast walking speeds around a variety of obstacles while instrumented with three IMUs (attached on the trunk, left and right shank). Raw data from 360 trials were analyzed. Compared to visual classification, the two IMU methods’ sensitivity/specificity to detecting spin turns were 76.1%/76.7% and 76.1%/84.4%, respectively. When the two methods were combined, the IMU had an overall 86.8% sensitivity and 92.2% specificity, with 89.4%/100% sensitivity/specificity at slow speeds. This combined method can be implemented into wireless fall prevention systems and used to identify increased use of spin turns. This method allows for longitudinal monitoring of turning strategies and allows researchers to test for potential associations between the frequency of spin turns and clinically relevant outcomes (e.g., falls) in non-laboratory settings

A deep learning approach towards railway safety risk assessment

Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks

Evaluation of Accelerometer-Based Walking-Turn Features for Fall-Risk Assessment in Older Adults

Falls in older adult populations are a serious health concern, resulting in physical and psychological trauma in addition to increased pressure on healthcare systems. Faller classification and fall risk assessment in elderly populations can facilitate preventative care before a fall occurs. Few research studies in the fall risk assessment field have focused on wearable-sensor-based features obtained during walking-turns. Examining turn based features may improve fall-risk assessment techniques. Seventy-six older individuals (74.15 ± 7.0 years), categorized as prospective fallers (28 participants) and non-fallers (43 participants), completed a six-minute walk test with accelerometers attached to their lower legs and pelvis. Turn and straight walking sections were segmented from the six-minute walk test, with a feature set extracted for each participant. This work aimed to determine if significant differences between prospective faller (PF) and non-faller (NF) groups existed for turn or straight walking features. A mixed-design ANOVA with post-hoc analysis showed no significant differences between faller groups for straight-walking features, while five turn based features had significant differences (p <0.05). These five turn based features were minimum of anterior-posterior REOH for right shank, SD of SD anterior left shank acceleration, SD of mean anterior left shank acceleration, maximum of medial-lateral FQFFT for lower back, and SD of maximum anterior left shank acceleration. Turn based features merit further investigation for distinguishing PF and NF. A novel prospective faller classification method was developed using accelerometer-based features from turns and straight walking. Cross validation was conducted for both turn and straight feature based models to assess classification performance. The best “classifier model – feature selector” combination used turn data, random forest classifier, and select-5-best feature selector (73.4% accuracy, 60.5% sensitivity, 82.0% specificity, 0.44 Matthew’s Correlation Coefficient (MCC)). Using only the most frequently occurring features, a feature subset achieved better classification results, with 77.3% accuracy, 66.1% sensitivity, 84.7% specificity, and 0.52 MCC score (minimum of anterior-posterior ratio of even/odd harmonics for right shank, standard deviation (SD) of anterior left shank acceleration SD, SD of mean anterior left shank acceleration, maximum of medial-lateral first quartile of Fourier transform (FQFFT) for lower back, maximum of anterior-posterior FQFFT for lower back). All classification performance metrics improved when turn data was used for faller classification, compared to straight walking data

Gait characterization using wearable inertial sensors in healthy and pathological populations

Gait analysis is emerging as an effective tool to detect an incipient neurodegenerative disease or to monitor its progression. It has been shown that gait disturbances are an early indicator for cognitive impairments and can predict progression to neurodegenerative diseases. Furthermore, gait performance is a predictor of fall status, morbidity and mortality. Instrumented gait analysis provides quantitative measures to support the investigation of gait pathologies and the definition of targeted rehabilitation programs. In this framework, technologies such as inertial sensors are well accepted, and increasingly employed, as tools to characterize locomotion patterns and their variability in research settings. The general aim of this thesis is the evaluation, comparison and refinement of methods for gait characterization using magneto-inertial measurement units (MIMUs), in order to contribute to the migration of instrumented gait analysis from state of the art to state of the science (i.e.: from research towards its application in standard clinical practice). At first, methods for the estimation of spatio-temporal parameters during straight gait were investigated. Such parameters are in fact generally recognized as key metrics for an objective evaluation of gait and a quantitative assessment of clinical outcomes. Although several methods for their estimate have been proposed, few provided a thorough validation. Therefore an error analysis across different pathologies, multiple clinical centers and large sample size was conducted to further validate a previously presented method (TEADRIP). Results confirmed the applicability and robustness of the TEADRIP method. The combination of good performance, reliability and range of usage indicate that the TEADRIP method can be effectively adopted for gait spatio-temporal parameter estimation in the routine clinical practice. However, while traditionally gait analysis is applied to straight walking, several clinical motor tests include turns between straight gait segments. Furthermore, turning is used to evaluate subjects’ motor ability in more challenging circumstances. The second part of the research therefore headed towards the application of gait analysis on turning, both to segment it (i.e.: distinguish turns and straight walking bouts) and to specifically characterize it. Methods for turn identification based on a single MIMU attached to the trunk were implemented and their performance across pathological populations was evaluated. Focusing on Parkinson’s Disease (PD) subjects, turn characterization was also addressed in terms of onset and duration, using MIMUs positioned both on the trunk and on the ankles. Results showed that in PD population turn characterization with the sensors at the ankles lacks of precision, but that a single MIMU positioned on the low back is functional for turn identification. The development and validation of the methods considered in these works allowed for their application to clinical studies, in particular supporting the spatio-temporal parameters analysis in a PD treatment assessment and the investigation of turning characteristic in PD subjects with Freezing of Gait. In the first application, comparing the pre and post parameters it was possible to objectively determine the effectiveness of a rehabilitation treatment. In the second application, quantitative measures confirmed that in PD subjects with Freezing of Gait turning 360° in place is further compromised (and requires additional cognitive effort) compared to turning 180° while walking

Standardizing the Calculation of the Lyapunov Exponent for Human Gait using Inertial Measurement Units

abstract: There are many inconsistencies in the literature regarding how to estimate the Lyapunov Exponent (LyE) for gait. In the last decade, many papers have been published using Lyapunov Exponents to determine differences between young healthy and elderly adults and healthy and frail older adults. However, the differences in methodologies of data collection, input parameters, and algorithms used for the LyE calculation has led to conflicting numerical values for the literature to build upon. Without a unified methodology for calculating the LyE, researchers can only look at the trends found in studies. For instance, LyE is generally lower for young adults compared to elderly adults, but these values cannot be correlated across studies to create a classifier for individuals that are healthy or at-risk of falling. These issues could potentially be solved by standardizing the process of computing the LyE. This dissertation examined several hurdles that must be overcome to create a standardized method of calculating the LyE for gait data when collected with an accelerometer. In each of the following investigations, both the Rosenstein et al. and Wolf et al. algorithms as well as three normalization methods were applied in order to understand the extent at which these factors affect the LyE. First, the a priori parameters of time delay and embedding dimension which are required for phase space reconstruction were investigated. This study found that the time delay can be standardized to a value of 10 and that an embedding dimension of 5 or 7 should be used for the Rosenstein and Wolf algorithm respectively. Next, the effect of data length on the LyE was examined using 30 to 1300 strides of gait data. This analysis found that comparisons across papers are only possible when similar amounts of data are used but comparing across normalization methods is not recommended. And finally, the reliability and minimum required number of strides for each of the 6 algorithm-normalization method combinations in both young healthy and elderly adults was evaluated. This research found that the Rosenstein algorithm was more reliable and required fewer strides for the calculation of the LyE for an accelerometer.Dissertation/ThesisAppendix ADoctoral Dissertation Biomedical Engineering 201

Effects of Direction Time Constraints and Walking Speed on Turn Strategies and Gait Adaptations in Healthy Older and Young Adults

Hip fractures can be life-threatening, debilitating, and costly. The odds for hip fracture increases from impact of sideways falls. While turning has been strongly associated with hip fracture & sideways falls, the distinction between the risks for walking-turns as opposed to low-velocity in-place turning is not clear. The present study sought to fill a gap as previous research had not compared walking-turn performance in young & healthy older adults at low-fall risk within the same study and response-conditions of speed interacting with direction-cue time constraints. Spatial-temporal variables representative of AP braking/propulsion (i.e. stride-length & speed) & ML stability (left/right H-H BOS) were collected with the Gaitrite upon approach of a turning zone whose entrance width was just 73 cm; and turn-strategy categorical data for stable wide-BOS step-turns, biomechanically challenging narrow-BOS spin-turns, and combined subtypes of mixed-turns either of the “extra-step” variety representative of an AP stability/braking issue or “small-amplitude” variety representative of a ML stability/balance issue were captured on video. Mixed-ANOVA of gait measures for AP propulsion/braking revealed no age-group differences in speed despite a trend for less of a fast-pace increase in elderly stride-length, yet similar anticipatory slowing and shorter strides approaching turns. Measures of ML stability revealed similar anticipatory widening of right BOS approaching turns, and a three-way interaction showed both had similar anticipatory narrowing of left BOS when approaching turns at fast-pace and similar reactive narrowing of left BOS following an unexpected turn-cue at preferred pace. Loglinear analysis of turn-strategies revealed no age-related associations as both preferred mixed-turns the least. At fast speeds preference for spin-turns decreased, yet when late-cued preference for both step-turns and spin-turns decreased 5.5-fold & 4.0-fold, respectively, indicating other factors besides biomechanical. Furthermore, the standardized residual reached significance for the elderly mixed-turns cell at the most constrained fast-speed*late-cue response-condition, with the “extra-step” sub-type contributing greatest possibly implying an AP rather than ML stability issue. The findings suggest that when approaching turns across an interaction of response-time conditions, healthy older adults show similar anticipatory/reactive gait adaptations and turn-strategy preferences with regards to AP propulsion/deceleration and ML stability/balance. In conclusion, within study limits, fall-prevention gait-training for healthy elderly with low-fall-risk and no age-related speed declines, in addition to addressing important ML stability issues of turn execution, are best served by not losing sight of the fundamental prerequisite to arrest forward momentum upon approach, and being inclusive of spin-turns for their ML space-efficiency