Gait Asymmetry Post-Stroke: Determining Valid and Reliable Methods Using a Single Accelerometer Located on the Trunk.

Asymmetry is a cardinal symptom of gait post-stroke that is targeted during rehabilitation. Technological developments have allowed accelerometers to be a feasible tool to provide digital gait variables. Many acceleration-derived variables are proposed to measure gait asymmetry. Despite a need for accurate calculation, no consensus exists for what is the most valid and reliable variable. Using an instrumented walkway (GaitRite) as the reference standard, this study compared the validity and reliability of multiple acceleration-derived asymmetry variables. Twenty-five post-stroke participants performed repeated walks over GaitRite whilst wearing a tri-axial accelerometer (Axivity AX3) on their lower back, on two occasions, one week apart. Harmonic ratio, autocorrelation, gait symmetry index, phase plots, acceleration, and jerk root mean square were calculated from the acceleration signals. Test-retest reliability was calculated, and concurrent validity was estimated by comparison with GaitRite. The strongest concurrent validity was obtained from step regularity from the vertical signal, which also recorded excellent test-retest reliability (Spearman's rank correlation coefficients (rho) = 0.87 and Intraclass correlation coefficient (ICC21) = 0.98, respectively). Future research should test the responsiveness of this and other step asymmetry variables to quantify change during recovery and the effect of rehabilitative interventions for consideration as digital biomarkers to quantify gait asymmetry.

Eye-tracker algorithms to detect saccades during static and dynamic tasks: a structured review.

OBJECTIVE: Eye-tracking devices have become widely used as clinical assessment tools in a variety of applied-scientific fields to measure saccadic eye movements. With the emergence of multiple static and dynamic devices, the concurrent need for algorithm development and validation is paramount. APPROACH: This review assesses the prevalence of current saccade detection algorithms, their associated validation methodologies and the suitability of their application. Medline, Embase, PsychInfo, Scopus, IEEEXplore and ACM Digital Library databases were searched. Two independent reviewers and an adjudicator screened articles describing the detection of saccades from raw infrared/video-based eye-tracker data. MAIN RESULTS: Thirteen articles were screened and met the inclusion criteria. Overall, the majority of reviewed saccadic detection algorithms used simple velocity-based classifications with static eye-tracking systems. Studies demonstrated validity but are limited by the static nature of testing. Heterogeneity in system design, proprietary and bespoke algorithmic methods used, processing strategies, and outcome reporting is evident. SIGNIFICANCE: This paper suggests the use of a more standardised methodology to facilitate experimental validity and improve comparison of results across studies.

Gait in Mild Alzheimer's Disease: Feasibility of Multi-Center Measurement in the Clinic and Home with Body-Worn Sensors: A Pilot Study.

Gait is emerging as a potential diagnostic tool for cognitive decline. The 'Deep and Frequent Phenotyping for Experimental Medicine in Dementia Study' (D&FP) is a multicenter feasibility study embedded in the United Kingdom Dementia Platform designed to determine participant acceptability and feasibility of extensive and repeated phenotyping to determine the optimal combination of biomarkers to detect disease progression and identify early risk of Alzheimer's disease (AD). Gait is included as a clinical biomarker. The tools to quantify gait in the clinic and home, and suitability for multi-center application have not been examined. Six centers from the National Institute for Health Research Translational Research Collaboration in Dementia initiative recruited 20 individuals with early onset AD. Participants wore a single wearable (tri-axial accelerometer) and completed both clinic-based and free-living gait assessment. A series of macro (behavioral) and micro (spatiotemporal) characteristics were derived from the resultant data using previously validated algorithms. Results indicate good participant acceptability, and potential for use of body-worn sensors in both the clinic and the home. Recommendations for future studies have been provided. Gait has been demonstrated to be a feasible and suitable measure, and future research should examine its suitability as a biomarker in AD.

Comprehensive measurement of stroke gait characteristics with a single accelerometer in the laboratory and community: a feasibility, validity and reliability study.

BACKGROUND: Application of objective measurement of stroke gait with accelerometer-based wearable technology and associated algorithms is increasing, despite reports questioning the accuracy of this technique in quantifying specific stroke-related gait impairments. The aim of this study is to determine the feasibility, validity and reliability of a low-cost open-source system incorporating algorithms and a single tri-axial accelerometer-based wearable to quantify gait characteristics in the laboratory and community post-stroke. METHODS: Twenty-five participants with stroke wore the wearable (AX3, Axivity) on the lower back during a laboratory 2 minute continuous walk (preferred pace) on two occasions a week apart and continuously in the community for two consecutive 7 day periods. Video, instrumented walkway (GaitRite) and an OPAL accelerometer-based wearable were used as laboratory references. RESULTS: Feasibility of the proposed system was good. The system was valid for measuring step count (ICC 0.899). Inherent differences in gait quantification between algorithm and GaitRite resulted in difficulties comparing agreement between the different systems. Agreement was moderate-excellent (ICC 0.503-0.936) for mean and variability gait characteristics vs. OPAL. Agreement was moderate-poor between the system and OPAL for asymmetry characteristics. Moderate-excellent reliability (ICC 0.534-0.857) was demonstrated for 11/14 laboratory measured gait characteristics. Community test-retest reliability was good-excellent (ICC 0.867-0.983) for all except one (ICC 0.699) of the 19 gait characteristics. CONCLUSION: The proposed system is a low-cost, reliable tool for quantifying gait post-stroke with multiple potential applications. Further refinement to optimise gait quantification algorithms for certain gait characteristics including gait asymmetry is required.

iTrack: instrumented mobile electrooculography (EOG) eye-tracking in older adults and Parkinson's disease.

Detection of saccades (fast eye-movements) within raw mobile electrooculography (EOG) data involves complex algorithms which typically process data acquired during seated static tasks only. Processing of data during dynamic tasks such as walking is relatively rare and complex, particularly in older adults or people with Parkinson's disease (PD). Development of algorithms that can be easily implemented to detect saccades is required. This study aimed to develop an algorithm for the detection and measurement of saccades in EOG data during static (sitting) and dynamic (walking) tasks, in older adults and PD. Eye-tracking via mobile EOG and infra-red (IR) eye-tracker (with video) was performed with a group of older adults (n = 10) and PD participants (n = 10) (⩾50 years). Horizontal saccades made between targets set 5°, 10° and 15° apart were first measured while seated. Horizontal saccades were then measured while a participant walked and executed a 40° turn left and right. The EOG algorithm was evaluated by comparing the number of correct saccade detections and agreement (ICC2,1) between output from visual inspection of eye-tracker videos and IR eye-tracker. The EOG algorithm detected 75-92% of saccades compared to video inspection and IR output during static testing, with fair to excellent agreement (ICC2,1 0.49-0.93). However, during walking EOG saccade detection reduced to 42-88% compared to video inspection or IR output, with poor to excellent (ICC2,1 0.13-0.88) agreement between methodologies. The algorithm was robust during seated testing but less so during walking, which was likely due to increased measurement and analysis error with a dynamic task. Future studies may consider a combination of EOG and IR for comprehensive measurement.

Detecting free-living steps and walking bouts: validating an algorithm for macro gait analysis.

Research suggests wearables and not instrumented walkways are better suited to quantify gait outcomes in clinic and free-living environments, providing a more comprehensive overview of walking due to continuous monitoring. Numerous validation studies in controlled settings exist, but few have examined the validity of wearables and associated algorithms for identifying and quantifying step counts and walking bouts in uncontrolled (free-living) environments. Studies which have examined free-living step and bout count validity found limited agreement due to variations in walking speed, changing terrain or task. Here we present a gait segmentation algorithm to define free-living step count and walking bouts from an open-source, high-resolution, accelerometer-based wearable (AX3, Axivity). Ten healthy participants (20-33 years) wore two portable gait measurement systems; a wearable accelerometer on the lower-back and a wearable body-mounted camera (GoPro HERO) on the chest, for 1 h on two separate occasions (24 h apart) during free-living activities. Step count and walking bouts were derived for both measurement systems and compared. For all participants during a total of almost 20 h of uncontrolled and unscripted free-living activity data, excellent relative (rho ⩾ 0.941) and absolute (ICC(2,1) ⩾ 0.975) agreement with no presence of bias were identified for step count compared to the camera (gold standard reference). Walking bout identification showed excellent relative (rho ⩾ 0.909) and absolute agreement (ICC(2,1) ⩾ 0.941) but demonstrated significant bias. The algorithm employed for identifying and quantifying steps and bouts from a single wearable accelerometer worn on the lower-back has been demonstrated to be valid and could be used for pragmatic gait analysis in prolonged uncontrolled free-living environments.

A model of free-living gait: A factor analysis in Parkinson's disease.

INTRODUCTION: Gait is a marker of global health, cognition and falls risk. Gait is complex, comprised of multiple characteristics sensitive to survival, age and pathology. Due to covariance amongst characteristics, conceptual gait models have been established to reduce redundancy and aid interpretation. Previous models have been derived from laboratory gait assessments which are costly in equipment and time. Body-worn monitors (BWM) allow for free-living, low-cost and continuous gait measurement and produce similar covariant gait characteristics. A BWM gait model from both controlled and free-living measurement has not yet been established, limiting utility. METHODS: 103 control and 67 PD participants completed a controlled laboratory assessment; walking for two minutes around a circuit wearing a BWM. 89 control and 58 PD participants were assessed in free-living, completing normal activities for 7 days wearing a BWM. Fourteen gait characteristics were derived from the BWM, selected according to a previous model. Principle component analysis derived factor loadings of gait characteristics. RESULTS: Four gait domains were derived for both groups and conditions; pace, rhythm, variability and asymmetry. Domains totalled 84.84% and 88.43% of variance for controlled and 90.00% and 93.03% of variance in free-living environments for control and PD participants respectively. Gait characteristic loading was unambiguous for all characteristics apart from gait variability which demonstrated cross-loading for both groups and environments. The model was highly congruent with the original model. CONCLUSIONS: The conceptual gait models remained stable using a BWM in controlled and free-living environments. The model became more discrete supporting utility of the gait model for free-living gait.

Validity of a wearable accelerometer to quantify gait in spinocerebellar ataxia type 6.

Biomarkers are required to track disease progression and measure the effectiveness of interventions for people with spinocerebellar ataxia type-6 (SCA6). Gait is a potential biomarker that is sensitive to SCA6 which can be measured using wearable technology, reducing the need for expensive specialist facilities. However, algorithms used to calculate gait using data from wearables have not been validated in SCA6. This study sought to examine the validity of a single wearable for deriving 14 spatio-temporal gait characteristics in SCA6 and control cohorts. Participants performed eight intermittent walks along a 7 m instrumented walkway at their preferred walking pace while also wearing a single accelerometer-based wearable on L5. Gait algorithms previously validated in neurological populations and controls were used to derive gait characteristics. We assessed the bias, agreement and sensitivity of gait characteristics derived using the instrumented walkway and the wearable. Mean gait characteristics showed good to excellent agreement for both groups, although gait variability and asymmetry showed poor agreement between the two systems. Agreement improved considerably in the SCA6 group when people who used walking sticks were excluded from the analysis, suggesting poorer agreement in people with more severe gait impairment. Despite poor agreement for some characteristics, gait measured using the wearable was generally more sensitive to group differences than the instrumented walkway. Our findings indicate mean gait characteristics can be accurately measured using an accelerometer-based wearable in people SCA6 with mild-to-moderately severe gait impairment yet further development of algorithms are required for people with more severe symptoms.

Measuring gait with an accelerometer-based wearable: influence of device location, testing protocol and age.

Wearables such as accelerometers are emerging as powerful tools for quantifying gait in various environments. Flexibility in wearable location may improve ease of use and data acquisition during instrumented testing. However, change of location may impact algorithm functionality when evaluating associated gait characteristics. Furthermore, this may be exacerbated by testing protocol (different walking speed) and age. Therefore, the aim of this study was to examine the effect of an accelerometer-based wearable(s) (accW) location, walking speed, age and algorithms on gait characteristics. Forty younger (YA) and 40 older adults (OA) were recruited. Participants wore accW positioned at the chest, waist and lower back (L5, gold standard) and were asked to walk continuously for 2 min at preferred and fast speeds. Two algorithms, previously validated for accW located on L5, were used to quantify step time and step length. Mean, variability and asymmetry gait characteristics were estimated for each location with reference to L5. To examine impact of locations and speed on algorithm-dependant characteristic evaluation, adjustments were made to the temporal algorithm. Absolute, relative agreement and difference between measurements at different locations and L5 were assessed. Mean step time and length evaluated from the chest showed excellent agreement compared to L5 for both age groups and speeds. Agreement between waist and L5 was excellent for mean step time for both speeds and age groups, good for mean step length at both speeds for YA and at preferred speed for OA. Step time and length asymmetry evaluated from the chest showed moderate agreement for YA only. Lastly, results showed that algorithm adjustment did not influence agreement between results obtained at different locations. Mean spatiotemporal characteristics can be robustly quantified from accW at the locations used in this study irrespective of speed and age; this is not true when estimating variability and asymmetry characteristics.

A multi-resolution investigation for postural transition detection and quantification using a single wearable.

BACKGROUND: Multi-resolution analyses involving wavelets are commonly applied to data derived from accelerometer-based wearable technologies (wearables) to identify and quantify postural transitions (PTs). Previous studies fail to provide rationale to inform their choice of wavelet and scale approximation when utilising discrete wavelet transforms. This study examines varying combinations of those parameters to identify best practice recommendations for detecting and quantifying sit-to-stand (SiSt) and stand-to-sit (StSi) PTs. METHODS: 39 young and 37 older participants completed three SiSt and StSi PTs on supported and unsupported chair types while wearing a single tri-axial accelerometer-based wearable on the lower back. Transition detection and duration were calculated through peak detection within the signal vector magnitude for a range of wavelets and scale approximations. A laboratory reference measure (2D video) was used for comparative analysis. RESULTS: Detection accuracy of wavelet and scale combinations for the transitions was excellent for both SiSt (87-97%) and StSi (82-86%) PT-types. The duration of PTs derived from the wearable showed considerable bias and poor agreement compared with the reference videos. No differences were observed between chair types and age groups respectively. CONCLUSIONS: Improved detection of PTs could be achieved through the incorporation of different wavelet and scale combinations for the assessment of specific PT types in clinical and free-living settings. An upper threshold of 5th scale approximations is advocated for improved detection of multiple PT-types. However, care should be taken estimating the duration of PTs using wearables.

Towards holistic free-living assessment in Parkinson's disease: Unification of gait and fall algorithms with a single accelerometer.

Technological developments have seen the miniaturization of sensors, small enough to be embedded in wearable devices facilitating unobtrusive and longitudinal monitoring in free-living environments. Concurrently, the advances in algorithms have been ad-hoc and fragmented. To advance the mainstream use of wearable technology and improved functionality of algorithms all methodologies must be unified and robustly tested within controlled and free-living conditions. Here we present and unify a (i) gait segmentation and analysis algorithm and (ii) a fall detection algorithm. We tested the unified algorithms on a cohort of young healthy adults within a laboratory. We then deployed the algorithms on longitudinal (7 day) accelerometer-based data from an older adult with Parkinson's disease (PD) to quantify real world gait and falls. We compared instrumented falls to a self-reported falls diary to test algorithm efficiency and discuss the use of unified algorithms to impact free-living assessment in PD where accurate recognition of gait may reduce the number of automated detected falls (38/week). This informs ongoing work to use gait and related outcomes as pragmatic clinical markers.

Toward a low-cost gait analysis system for clinical and free-living assessment.

Gait is an important clinical assessment tool since changes in gait may reflect changes in general health. Measurement of gait is a complex process which has been restricted to bespoke clinical facilities until recently. The use of inexpensive wearable technologies is an attractive alternative and offers the potential to assess gait in any environment. In this paper we present the development of a low cost analysis gait system built using entirely open source components. The system is used to capture spatio-temporal gait characteristics derived from an existing conceptual model, sensitive to ageing and neurodegenerative pathology (e.g. Parkinson's disease). We demonstrate the system is suitable for use in a clinical unit and will lead to pragmatic use in a free-living (home) environment. The system consists of a wearable (tri-axial accelerometer and gyroscope) with a Raspberry Pi module for data storage and analysis. This forms ongoing work to develop gait as a low cost diagnostic in modern healthcare.