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

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

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

Beyond the front end: Investigating a thigh worn accelerometer device for step count and bout detection in Parkinson's disease

Free-living ambulation with accelerometer-based devices is an attractive methodology to assess habitual behaviour within Parkinson's disease (PD). However, slowness of movement can contribute to difficulty in quantifying ambulatory/walking outcomes within this group by these devices. This study investigates the use of a commercial accelerometer device (activPAL™) in those with moderate PD to understand its proprietary software (inbuilt algorithm) limitations. The values provided by the proprietary software are evaluated in comparison to novel algorithms on the same raw data to examine limitations for use within this cohort. The bespoke algorithms help to alter sensitivity in outcomes stemming from the same accelerometer data while also highlighting how slight changes in algorithms can drastically inflate/deflate values. In general, results show that the proprietary software generally quantifies lower values of outcomes (step and bout count), which is similar to previous findings. Variations in algorithm functionality highlight large heterogeneity in bout and step counts resulting from a lack of how they are defined within the literature. The novel alternative ambulatory algorithms presented here should be considered for use on raw data from the activPAL™ in those with moderate/severe PD

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

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

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

Instrumented assessment of test battery for physical capability using an accelerometer: a feasibility study

Recent work has identified subdomains (tests) of physical capability that are recommended for assessment of the healthy ageing phenotype (HAP). These include: postural control, locomotion, endurance, repeated sit-to-stand-to-sit and TUG. Current assessment methods lack sensitivity and are error prone due to their lack of consistency and heterogeneity of reported outcomes; instrumentation with body worn monitors provides a method to address these potential weaknesses. This work proposes the use of a single tri-axial accelerometer-based device with appropriate algorithms (referred to here as a body worn monitor, BWM) for the purposes of instrumented testing during physicality capability assessment. In this pilot study we present 14 BWM-based outcomes across the subdomains which include magnitude, frequency and spatio-temporal characteristics. Where possible, we compared BWM outcomes with manually recorded values and found no significant differences between locomotion and TUG tasks (p ≥ 0.319). Significant differences were found for the total distance walked during endurance (p = 0.037) and times for repeated sit-to-stand-to-sit transitions (p < 0.000). We identified reasons for differences and make recommendations for future testing. We were also able to quantify additional characteristics of postural control and gait which could be sensitive outcomes for future HAP assessment. Our findings demonstrate the feasibility of this method to enhance measurement of physical capacity. The methodology can also be applied to a wide variety of accelerometer-based monitors and is applicable to a range of intervention-based studies or pathological assessment