Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium.

BACKGROUND: Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. METHODS: Twenty healthy older adults, 20 people with Parkinson's disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. RESULTS: We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms' performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms. CONCLUSIONS: Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms' performances. Trial registration ISRCTN - 12246987.

Design and validation of a multi-task, multi-context protocol for real-world gait simulation.

BACKGROUND: Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a protocol for simulating real-world gait accounting for all these factors within a single set of observations, while ensuring minimisation of participant burden and safety. METHODS: The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants' strategy of walking. It was deployed in a convenience sample of 108 participants recruited from six cohorts that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson's disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). A novelty introduced in the protocol was the tiered approach to increase difficulty both within the same task (e.g., by allowing use of aids or armrests) and across tasks. RESULTS: The protocol proved to be safe and feasible (all participants could complete it and no adverse events were recorded) and the addition of the more complex tasks allowed a much greater spread in walking speeds to be achieved compared to standard straight walking trials. Furthermore, it allowed a representation of a variety of daily life relevant mobility aspects and can therefore be used for the validation of monitoring devices used in real life. CONCLUSIONS: The protocol allowed for measuring gait in a variety of pathological conditions suggests that it can also be used to detect changes in gait due to, for example, the onset or progression of a disease, or due to therapy. TRIAL REGISTRATION: ISRCTN-12246987.

Robot-assisted training after proximal humeral fracture: A randomised controlled multicentre intervention trial.

OBJECTIVE: To examine whether robotic-assisted training as a supplement to usual therapy is safe, acceptable and improves function and patient reported outcome after proximal humeral fractures (PHF). DESIGN: Multicentre, assessor-blinded, randomised controlled prospective trial. SETTING: Three different rehabilitation hospitals in Germany. SUBJECTS: In total 928 PHF patients between 35 and 70 years were screened. Forty-eight participants were included in the study (intervention group n = 23; control group n = 25). INTERVENTION: The control group received usual occupational and physiotherapy over three weeks, and the intervention group received additional 12 robot-assisted training sessions at the ARMEO®-Spring. MAIN MEASURES: Disabilities of the Arm, Shoulder and Hand Questionnaire (DASH), the Wolf Motor Function Test-Orthopaedic, active range of motion and grip strength were determined before and after intervention period. The DASH was additionally obtained postal 6 and 13 months following surgery. RESULTS: The mean age of participants was 55 ± 10 years and was similar in both groups (p > 0.05). The change in DASH as the primary endpoint in the intervention group after intervention was -15 (CI = 8-22), at follow-up six month -7 (CI = -2 to 16) at follow up 13 month -9 (CI = 1-16); in control group -14 (CI = 11-18), at follow-up six month -13 (CI = 7-19) at follow up 13 month -6 (CI = -3 to 14). No difference in the change was found between groups (p > 0.05). None of the follow-up time points demonstrated an additional benefit of the robotic therapy. CONCLUSION: The additional robot-assisted therapy was safe, acceptable but showed no improvement in functional shoulder outcome compared to usual therapy only.

Estimate of gait speed by using persons' walk ratio or step-frequency in older adults.

BACKGROUND AND AIMS: Gait speed estimation using wearable inertial sensors during daily activities suffers from high complexity and inaccuracies in distance estimation when integrating acceleration signals. The aim of the study was to investigate the agreement between the methods of gait speed estimation using the persons' walk ratio (step-length/step-frequency relation) or step-frequency (number of steps per minute) and a "gold standard". METHODS: For this cross-sectional validation study, 20 healthy community-dwelling older persons (mean age 72.1 years; 70% women) walked at slow, normal, and fast speed over an instrumented walkway (reference measure). Gait speed was calculated using the person's pre-assessed walk ratio. Furthermore, the duration of walking and number of steps were used for calculation. RESULTS: The agreement between gait speed calculation using the walk ratio or step-frequency (adjusted to body height) and reference was r = 0.98 and r = 0.93, respectively. Absolute and relative mean errors of calculated gait speed using pre-assessed walk ratio ranged between 0.03-0.07 m/s and 1.97-4.17%, respectively. DISCUSSION AND CONCLUSIONS: After confirmation in larger cohorts of healthy community-dwelling older adults, the mean gait speed of single walking bouts during activity monitoring can be estimated using the person's pre-assessed walk ratio. Furthermore, the mean gait speed can be calculated using the step-frequency and body height and can be an additional parameter in stand-alone activity monitoring.

A Quality Control Check to Ensure Comparability of Stereophotogrammetric Data between Sessions and Systems.

Optoelectronic stereophotogrammetric (SP) systems are widely used in human movement research for clinical diagnostics, interventional applications, and as a reference system for validating alternative technologies. Regardless of the application, SP systems exhibit different random and systematic errors depending on camera specifications, system setup and laboratory environment, which hinders comparing SP data between sessions and across different systems. While many methods have been proposed to quantify and report the errors of SP systems, they are rarely utilized due to their complexity and need for additional equipment. In response, an easy-to-use quality control (QC) check has been designed that can be completed immediately prior to a data collection. This QC check requires minimal training for the operator and no additional equipment. In addition, a custom graphical user interface ensures automatic processing of the errors in an easy-to-read format for immediate interpretation. On initial deployment in a multicentric study, the check (i) proved to be feasible to perform in a short timeframe with minimal burden to the operator, and (ii) quantified the level of random and systematic errors between sessions and systems, ensuring comparability of data in a variety of protocol setups, including repeated measures, longitudinal studies and multicentric studies.

Re-Enactment as a Method to Reproduce Real-World Fall Events Using Inertial Sensor Data: Development and Usability Study.

BACKGROUND: Falls are a common health problem, which in the worst cases can lead to death. To develop reliable fall detection algorithms as well as suitable prevention interventions, it is important to understand circumstances and characteristics of real-world fall events. Although falls are common, they are seldom observed, and reports are often biased. Wearable inertial sensors provide an objective approach to capture real-world fall signals. However, it is difficult to directly derive visualization and interpretation of body movements from the fall signals, and corresponding video data is rarely available. OBJECTIVE: The re-enactment method uses available information from inertial sensors to simulate fall events, replicate the data, validate the simulation, and thereby enable a more precise description of the fall event. The aim of this paper is to describe this method and demonstrate the validity of the re-enactment approach. METHODS: Real-world fall data, measured by inertial sensors attached to the lower back, were selected from the Fall Repository for the Design of Smart and Self-Adaptive Environments Prolonging Independent Living (FARSEEING) database. We focused on well-described fall events such as stumbling to be re-enacted under safe conditions in a laboratory setting. For the purposes of exemplification, we selected the acceleration signal of one fall event to establish a detailed simulation protocol based on identified postures and trunk movement sequences. The subsequent re-enactment experiments were recorded with comparable inertial sensor configurations as well as synchronized video cameras to analyze the movement behavior in detail. The re-enacted sensor signals were then compared with the real-world signals to adapt the protocol and repeat the re-enactment method if necessary. The similarity between the simulated and the real-world fall signals was analyzed with a dynamic time warping algorithm, which enables the comparison of two temporal sequences varying in speed and timing. RESULTS: A fall example from the FARSEEING database was used to show the feasibility of producing a similar sensor signal with the re-enactment method. Although fall events were heterogeneous concerning chronological sequence and curve progression, it was possible to reproduce a good approximation of the motion of a person's center of mass during fall events based on the available sensor information. CONCLUSIONS: Re-enactment is a promising method to understand and visualize the biomechanics of inertial sensor-recorded real-world falls when performed in a suitable setup, especially if video data is not available.

Fall Risk in Relation to Individual Physical Activity Exposure in Patients with Different Neurodegenerative Diseases: a Pilot Study.

Falls in patients with neurodegenerative diseases (NDDs) have enormous detrimental consequences. A better understanding of the interplay between physical activity (PA) and fall risk might help to reduce fall frequency. We aimed to investigate the association between sensor-based PA and fall risk in NDDs, using "falls per individual PA exposure time" as a novel measure. Eighty-eight subjects (n = 31 degenerative ataxia (DA), n = 14 Parkinson's disease (PD), n = 12 progressive supranuclear palsy (PSP) and 31 healthy controls) were included in this pilot study. PA was recorded in free-living environments with three-axial accelerometers (activPAL™) over 7 days. Falls were prospectively assessed over 12 months. Fall incidence was calculated by (i) absolute number of falls per person years (py) and (ii) falls per exposure to individual PA. Absolute fall incidence was high in all three NDDs, with differing levels (DA, 9 falls/py; PD, 14 falls/py; PSP, 29 falls/py). Providing a more fine-grained view on fall risk, correction for individual exposure to PA revealed that measures of low walking PA were associated with higher fall incidence in all three NDDs. Additionally, higher fall incidence was associated with more sit-to-stand transfers in PD and longer walking bouts in PSP. Our results suggest that low walking PA is a risk factor for falls in DA, PD and PSP, indicating the potential benefit of increasing individual PA in these NDDs to reduce fall risk. Moreover, they show that correction for individual exposure to PA yields a more differentiated view on fall risk within and across NDDs.

Inter-rater reliability, sensitivity to change and responsiveness of the orthopaedic Wolf-Motor-Function-Test as functional capacity measure before and after rehabilitation in patients with proximal humeral fractures.

BACKGROUND: The incidence of proximal humeral fractures (PHF) increased by more than 30% over the last decade, which is accompanied by an increased number of operations. However, the evidence on operative vs. non-operative treatment and post-operative treatments is limited and mostly based on expert opinion. It is mandatory to objectively assess functional capacity to compare different treatments. Clinical tools should be valid, reliable and sensitive to change assessing functional capacity after PHFs. This study aimed to analyse inter-rater reliability of the videotaped Wolf-Motor-Function-Test-Orthopaedic (WMFT-O) and the association between the clinical WMFT-O and the Disability of the Arm, Shoulder and Hand (DASH) and to determine the sensitivity to change of the WMFT-O and the DASH to measure functional capacity before and after rehabilitation in PHF patients. METHODS: Fifty-six patients (61.7 ± 14.7 years) after surgical treatment of PHF were assessed using the WMFT-O at two different time points. To determine inter-rater reliability, the videotaped WMFT-O was evaluated through three blinded raters. Inter-rater agreement was determined by Fleiss' Kappa statistics. Pearson correlation coefficients were calculated to assess the association between the clinical WMFT-O and the video rating as well as the DASH. Sensitivity to change and responsiveness were analysed for the WMFT-O and the DASH in a subsample of forty patients (53.8 ± 1.4 years) who were assessed before and after a three week robotic-assisted training intervention. RESULTS: Inter-rater agreement was indicated by Fleiss' Kappa values ranging from 0.33-0.66 for functional capacity and from 0.27-0.54 for quality of movement. The correlation between the clinical WMFT-O and the video rating was higher than 0.77. The correlation between the clinical WMFT-O and the DASH was weak. Sensitivity to change was high for the WMFT-O and the DASH and responsiveness was given. In comparison to the DASH, the sensitivity to change of the WMFT-O was higher. CONCLUSION: The overall results indicate that the WMFT-O is a reliable, sensitive and responsive instrument to measure more objectively functional change over time in rehabilitation after PHF. Furthermore, it has been shown that video assessment is eligible for studies to ensure a full blinding of raters. TRIAL REGISTRATION: Clinicaltrials.gov, NCT03100201 . Registered on 28 March 2017. The trial was retrospectively registered.

Change of Objectively-Measured Physical Activity during Geriatric Rehabilitation.

This prospective study investigated feasibility and sensitivity of sensor-based physical activity (PA) measures to monitor changes in PA during geriatric rehabilitation and its relation to clinical parameters at admission. PA was routinely measured at day 2 and day 15 after admission in 647 patients (70.2% women, mean age = 82.0 (SD = 7.19) years) of a German geriatric hospital using a thigh-worn accelerometer. Clinical records were used to include age, Barthel Index, diagnosis, mobility, orientation and cognition. Mean values and 95% confidence intervals (95%-CI) of walking duration, walking bout duration and number of sit-to-stand transfers were calculated to quantify different domains of PA. All observed PA parameters improved during rehabilitation, regardless of age, diagnosis or physical and cognitive function at admission. Walking duration increased by 12.1 (95%-CI: 10.3; 13.8) min, walking bout duration by 2.39 (95%-CI: 1.77; 3.00) s, and number of sit-to-stand transfers by 7 (95%-CI: 5; 8). Floor and ceiling effects were not observed. Walking duration at day 2 as well as day 15 was continuously associated with Barthel Index and statistically significant improved for all levels of Barthel Index. In summary, this study showed that sensor-based PA monitoring is feasible to assess the individual progress in geriatric rehabilitation patients.

Reading from the Black Box: What Sensors Tell Us about Resting and Recovery after Real-World Falls.

BACKGROUND: Lying on the floor for a long time after falls, regardless of whether an injury results, remains an unsolved health care problem. In order to develop efficient and acceptable fall detection and reaction approaches, it is relevant to improve the understanding of the circumstances and the characteristics of post-impact responses and the return or failure to return to pre-fall activities. Falls are seldom observed by others; until now, the knowledge about movement kinematics during falls and following impact have been anecdotal. OBJECTIVE: This study aimed to analyse characteristics of the on-ground and recovery phases after real-world falls. The aim was to compare self-recovered falls (defined as returns to standing from the floor) and non-recovered falls with long lies. METHODS AND PARTICIPANTS: Data from subjects in different settings and of different populations with high fall risk were included. Real-world falls collected by inertial sensors worn on the lower back were taken from the FARSEEING database if reliable information was available from fall reports and sensor signals. Trunk pitch angle and acceleration were analysed to describe different patterns of recovery movements while standing up from the floor after the impact of a fall. RESULTS: Falls with successful recovery, where an upright posture was regained, were different from non-recovered falls in terms of resting duration (median 10.5 vs. 34.5 s, p = 0.045). A resting duration longer than 24.5 s (area under the curve = 0.796) after the fall impact was a predictor for the inability to recover to standing. Successful recovery to standing showed lower cumulative angular pitch movement than attempted recovery in fallers that did not return to a standing position (median = 76°, interquartile range 24-170° vs. median = 308°, interquartile range 30-1,209°, p = 0.06). CONCLUSION: Fall signals with and without successful returns to standing showed different patterns during the phase on the ground. Characteristics of real-world falls provided through inertial sensors are relevant to improve the classification and the sensing of falls. The findings are also important for redesigning emergency response processes after falls in order to better support individuals in case of an unrecovered fall. This is crucial for preventing long lies and other fall-related incidents that require an automated fall alarm.

Inter-rater and intra-rater reliability of an adapted Wolf motor function test for older patients with shoulder injuries.

BACKGROUND AND OBJECTIVE: The Wolf motor function test (WMFT) evaluates the upper extremity performance of patients with neurological disorders and traumatic brain injuries by assessing time and functional capacity. It was later modified to also measure the quality of the performed movements. This study aimed to adapt the WMFT for patients with shoulder injuries and to evaluate the inter-rater and intra-rater reliability of this adapted version. SUBJECTS AND METHODS: A total of 20 individuals with a median age of 80.5 years were assessed using a test-retest design after surgical or conservative treatment of shoulder injuries. Two raters rated performance to determine inter-rater reliability. One rater rated the performance again to determine intra-rater reliability. Both inter-rater and intra-rater agreement were determined by weighted Cohen's kappa statistics with corresponding confidence intervals. Cronbach's alpha was calculated to determine internal consistency. RESULTS: The inter-rater Cohen's kappa values ranged from 0.84-1.00 for functional capacity and from 0.79-1.00 for quality of movement. For intra-rater reliability the Cohen's kappa ranged from 0.71-1.00. Cronbach's alpha was >0.94 for functional capacity and >0.96 for quality of movement. CONCLUSION: The adapted version of the WMFT shows high intra-rater and inter-rater reliability for patients with shoulder injuries.

Ambulatory Activity Components Deteriorate Differently across Neurodegenerative Diseases: A Cross-Sectional Sensor-Based Study.

BACKGROUND AND PURPOSE: Reduced ambulatory activity is a major burden in neurodegenerative disease (NDD), leading to severe restrictions in social participation and further deterioration of motor capacities. However, objective evidence on walking behavior patterns and components underlying this impairment and its decline with disease progression is scarce for many NDDs. We aimed to unravel the detailed metrics underlying the reduced ambulatory activity in selected NDDs, and their relation to disease duration. We hypothesized that progressively reduced ambulatory activity is a feature shared across different NDDs, characterized by changes in both common and distinct components. METHODS: Sixty-five subjects with NDD (n = 34 degenerative ataxia; n = 15 progressive supranuclear palsy, and n = 16 Parkinson's disease) and 38 healthy older adults (total n = 103) wore a three-axial accelerometer (activPAL3™) for 7 consecutive days. Detailed metrics of ambulatory activity were calculated. RESULTS: The average daily walking duration was significantly decreased in all three NDDs, yet characterized by a differential pattern of changes in number and length of walking bouts and sit-to-stand transfers. Decline in walking duration progressed with increased disease duration in all three NDDs, yet at a differing rate. This decline was associated with progressive reductions in walking bout length and walking behavior pattern diversity in all three NDDs. CONCLUSIONS: These findings provide objective evidence that reduced ambulatory activity is a shared feature across different NDDs. Moreover, they reveal that several underlying walking behavior components change with increasing disease duration, yet at a differing rate in different NDDs. This indicates that metric analysis of ambulatory activity might provide ecologically relevant and disease-specific progression and outcome markers in several NDDs.

Inertial Sensor Based Analysis of Lie-to-Stand Transfers in Younger and Older Adults.

Many older adults lack the capacity to stand up again after a fall. Therefore, to analyse falls it is relevant to understand recovery patterns, including successful and failed attempts to get up from the floor in general. This study analysed different kinematic features of standing up from the floor. We used inertial sensors to describe the kinematics of lie-to-stand transfer patterns of younger and healthy older adults. Fourteen younger (20-50 years of age, 50% men) and 10 healthy older community dwellers (≥60 years; 50% men) conducted four lie-to-stand transfers from different initial lying postures. The analysed temporal, kinematic, and elliptic fitting complexity measures of transfer performance were significantly different between younger and older subjects (i.e., transfer duration, angular velocity (RMS), maximum vertical acceleration, maximum vertical velocity, smoothness, fluency, ellipse width, angle between ellipses). These results show the feasibility and potential of analysing kinematic features to describe the lie-to-stand transfer performance, to help design interventions and detection approaches to prevent long lies after falls. It is possible to describe age-related differences in lie-to-stand transfer performance using inertial sensors. The kinematic analysis remains to be tested on patterns after real-world falls.

Association between vestibulo-ocular reflex suppression, balance, gait, and fall risk in ageing and neurodegenerative disease: protocol of a one-year prospective follow-up study.

BACKGROUND: Falls frequency increases with age and particularly in neurogeriatric cohorts. The interplay between eye movements and locomotion may contribute substantially to the occurrence of falls, but is hardly investigated. This paper provides an overview of current approaches to simultaneously measure eye and body movements, particularly for analyzing the association of vestibulo-ocular reflex (VOR) suppression, postural deficits and falls in neurogeriatric risk cohorts. Moreover, VOR suppression is measured during head-fixed target presentation and during gaze shifting while postural control is challenged. Using these approaches, we aim at identifying quantitative parameters of eye-head-coordination during postural balance and gait, as indicators of fall risk. METHODS/DESIGN: Patients with Progressive Supranuclear Palsy (PSP) or Parkinson's disease (PD), age- and sex-matched healthy older adults, and a cohort of young healthy adults will be recruited. Baseline assessment will include a detailed clinical assessment, covering medical history, neurological examination, disease specific clinical rating scales, falls-related self-efficacy, activities of daily living, neuro-psychological screening, assessment of mobility function and a questionnaire for retrospective falls. Moreover, participants will simultaneously perform eye and head movements (fixating a head-fixed target vs. shifting gaze to light emitting diodes in order to quantify vestibulo-ocular reflex suppression ability) under different conditions (sitting, standing, or walking). An eye/head tracker synchronized with a 3-D motion analysis system will be used to quantify parameters related to eye-head-coordination, postural balance, and gait. Established outcome parameters related to VOR suppression ability (e.g., gain, saccadic reaction time, frequency of saccades) and motor related fall risk (e.g., step-time variability, postural sway) will be calculated. Falls will be assessed prospectively over 12 months via protocols and monthly telephone interviews. DISCUSSION: This study protocol describes an experimental setup allowing the analysis of simultaneously assessed eye, head and body movements. Results will improve our understanding of the influence of the interplay between eye, head and body movements on falls in geriatric high-risk cohorts.

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device.

This study aimed to validate a wearable device's walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson's Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and - 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application.Trial registration: ISRCTN - 12246987.

Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study.

BACKGROUND: Wrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies. OBJECTIVE: The aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back-worn inertial sensors. METHODS: Participants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back-worn inertial sensors. RESULTS: The best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids. CONCLUSIONS: Algorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health. TRIAL REGISTRATION: ISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-050785.

Acceptability of wearable devices for measuring mobility remotely: Observations from the Mobilise-D technical validation study.

Background: This study aimed to explore the acceptability of a wearable device for remotely measuring mobility in the Mobilise-D technical validation study (TVS), and to explore the acceptability of using digital tools to monitor health. Methods: Participants (N = 106) in the TVS wore a waist-worn device (McRoberts Dynaport MM + ) for one week. Following this, acceptability of the device was measured using two questionnaires: The Comfort Rating Scale (CRS) and a previously validated questionnaire. A subset of participants (n = 36) also completed semi-structured interviews to further determine device acceptability and to explore their opinions of the use of digital tools to monitor their health. Questionnaire results were analysed descriptively and interviews using a content analysis. Results: The device was considered both comfortable (median CRS (IQR; min-max) = 0.0 (0.0; 0-20) on a scale from 0-20 where lower scores signify better comfort) and acceptable (5.0 (0.5; 3.0-5.0) on a scale from 1-5 where higher scores signify better acceptability). Interviews showed it was easy to use, did not interfere with daily activities, and was comfortable. The following themes emerged from participants' as being important to digital technology: altered expectations for themselves, the use of technology, trust, and communication with healthcare professionals. Conclusions: Digital tools may bridge existing communication gaps between patients and clinicians and participants are open to this. This work indicates that waist-worn devices are supported, but further work with patient advisors should be undertaken to understand some of the key issues highlighted. This will form part of the ongoing work of the Mobilise-D consortium.

A multi-sensor wearable system for the assessment of diseased gait in real-world conditions.

Introduction: Accurately assessing people's gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). Methods: The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity. Results and discussion: Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02 m, walking speed ≤0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72-4.87 steps/min, stride length 0.04-0.06 m, walking speed 0.03-0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.

Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases.

Introduction: The clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings. Methods: Here, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data. Results and discussion: The results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of -0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, -0.07, and <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases.