Real-world Walking Research Articles

Perturbations During Gait on a Split-Belt Treadmill: A Scoping Review

Background: Humans encounter disturbances like slips, pushes, and trips while walking, mainly from external forces. Technological advances have improved methods to study these impacts on gait, with split-belt treadmills being particularly effective. This scoping review aims to examine the types of perturbations used during split-belt treadmill gait, explore the methods used to induce them, and consolidate current knowledge on the effects of split-belt treadmill-induced gait perturbations. Methods: The review included publications from January 2015 to May 2024, as searched via PubMed, EBSCO, and ScienceDirect. Results: The review examined 33 studies on split-belt treadmills, focusing on perturbations like slip-like, trip-like, lateral displacements, and tilts, with speed changes being the most common. Perturbations were mainly applied during initial contact. The results show that young, healthy adults adapt quickly to anticipatory and reactive adjustments, while older adults and those with neurological impairments use less efficient strategies like increased muscular co-contraction. Asymmetrical gait adaptations persist after perturbations, highlighting motor learning and the role of the central nervous system and sensory feedback. Conclusions: Despite their precision, split-belt and tilting treadmills may not fully replicate real-world walking complexities. The review highlights the strengths and limitations of split-belt treadmills, emphasizing the need to integrate diverse methods to enhance rehabilitation and improve gait stability.

Installation of Public Access Defibrillators for Out-of-Hospital Cardiac Arrests: Identifying Suitable Locations by Using a Geographic Information System.

Survival following an out-of-hospital cardiac arrest depends on prompt defibrillation. Despite the efforts made to install automated external defibrillators (AEDs) in crowded areas, their usage rate remains suboptimal. This study evaluated the efficiency of installing AEDs at key landmarks in Taoyuan City to enhance accessibility and usage. This retrospective cohort study analyzed nontraumatic public out-of-hospital cardiac arrest cases in Taoyuan City from 2017 to 2021, using data from the Taoyuan Fire Department and a regional registry. AED data were collected for 1163 devices. A geographic information system mapped target locations within the city, and real-world walking routes were examined to assess coverage. The primary outcome was actual coverage and the coverage efficiency ratio, calculated as the actual coverage divided by the number of facilities at a location. The coverage efficiency ratio compared the coverage efficiency of target locations with existing public access defibrillators (PADs). Top locations for superior coverage in both downtown and outside downtown areas were bus stops and convenience stores (7-Eleven and FamilyMart), which outperformed existing PADs. Convenience stores had a higher coverage efficiency ratio than the public service sector. Bus stops showed high AED usage rates before ambulance arrival. The current PAD locations in Taoyuan City offer limited coverage, which highlights the need for strategically installed AEDs, particularly in convenience stores. Policymakers should consider using the cultural relevance and accessibility of convenience stores, particularly 7-Eleven branches, to enhance AED usage rates.

Experience from Designing Augmented Reality Browsing Interfaces for Real-world Walking Scenarios

Mobile phones have enabled users to browse information in varying mobility contexts. For high-mobility settings such as walking, however, phones pose several usability challenges, particularly safety and limited screen sizes. While Augmented Reality (AR) has been proposed to address these issues, prior work has yet to investigate AR interface design in real-world walking conditions beyond text readability and notification design. This paper presents the first exploration of AR browsing interface design and extended usage while walking in the wild. We first conducted design sessions with 12 UI designers while walking in varied environments to design the window size, distance, opacity, anchor type, and placement for three categories of apps: text, video, and mixed content. Results show that traffic level significantly affects the designed window size, whereas content type significantly affects window size, distance, opacity, and vertical placement. To gain further insights from real-world usage, we conducted a multi-day observational study with 5 participants and observed that participants on average switched among window layouts every 3.3 minutes, for reasons such as safety and the level of extended visual attention.

168 Real-world walking of people with the chronic lung disease COPD

Abstract Purpose Walking is crucial for active and healthy ageing, but it changes with age and in the presence of diverse health conditions, such as non-communicable diseases. In the field of the chronic lung disease COPD (chronic obstructive pulmonary disease), extensive research has been done on physical activity limitation, but little is known about the patterns of walking during daily life (i.e., real-world). We aimed to assess the levels and distribution of real-world walking (walking activity and gait parameters) in people with COPD compared with healthy adults, and across disease severity; and to identify domains of walking in COPD, using a single wearable device. Methods In a cross-sectional study, 550 people with COPD (37% women, with a mean (SD) age of 68 (8) years, postbronchodilator FEV1 54 (21) %) and 19 age-matched healthy adults were recruited in ten cities across eight countries. Subjects were given a single wearable device to wear at their lower back for one week. Twenty-five walking activity and gait parameters (e.g., number of steps, number of walking bouts (WB)>30s, walking speed in WB > 30s, cadence in WB > 30s, walking speed and cadence variability) were derived from the device outputs. Results Most walking activity and gait parameters were reduced in people with COPD compared to healthy adults, and according to COPD severity. Differences remained statistically significant after adjusting for age, sex, height and comorbidities in multivariable analyses. Factor analysis identified six distinct walking domains: Amount of walking, Patterns of walking, Pace, Rhythm, Pace variability and Rhythm variability. Conclusion Real-world walking in people with COPD is a multi-dimensional behaviour that is impaired as the disease progresses. These results may be used to set priorities and improve patient centricity in clinical practice, research and public health initiatives. Support/Funding Source This work was supported by the Mobilise-D project that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No. 820820. This JU receives support from the European Union’s Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA).

Cortical activation among young adults during mobility in an indoor real-world environment: A mobile EEG approach

Human mobility requires neurocognitive inputs to safely navigate the environment. Previous research has examined neural processes that underly walking using mobile neuroimaging technologies, yet few studies have incorporated true real-world methods without a specific task imposed on participants (e.g., dual-task, motor demands). The present study included 40 young adults (M = 22.60, SD = 2.63, 24 female) and utilized mobile electroencephalography (EEG) to examine and compare theta, alpha, and beta frequency band power (μV2) during sitting and walking in laboratory and real-world environments. EEG data was recorded using the Muse S brain sensing headband, a portable system equipped with four electrodes (two frontal, two temporal) and one reference sensor. Qualitative data detailing the thoughts of each participant were collected after each condition. For the quantitative data, a 2 × 2 repeated measures ANOVA with within subject factors of environment and mobility was conducted with full participant datasets (n = 17, M = 22.59, SD = 2.97, 10 female). Thematic analysis was performed on the qualitative data (n = 40). Our findings support that mobility and environment may modulate neural activity, as we observed increased brain activation for walking compared to sitting, and for real-world walking compared to laboratory walking. We identified five qualitative themes across the four conditions 1) physical sensations and bodily awareness, 2) responsibilities and planning, 3) environmental awareness, 4) mobility, and 5) spotlight effect. Our study highlights the importance and potential for real-world methods to supplement standard research practices to increase the ecological validity of studies conducted in the fields of neuroscience and kinesiology.

Understanding the influence of context on real-world walking energetics.

Speeds that minimize energetic cost during steady-state walking have been observed during lab-based investigations of walking biomechanics and energetics. However, in real-world scenarios, humans walk in a variety of contexts that can elicit different walking strategies, and may not always prioritize minimizing energetic cost. To investigate whether individuals tend to select energetically optimal speeds in real-world situations and how contextual factors influence gait, we conducted a study combining data from lab and real-world experiments. Walking kinematics and context were measured during daily life over a week (N=17) using wearable sensors and a mobile phone. To determine context, we utilized self-reported activity logs, GPS data and follow-up exit interviews. Additionally, we estimated energetic cost using respirometry over a range of gait speeds in the lab. Gross and net cost of transport were calculated for each participant, and were used to identify energetically optimal walking speed ranges for each participant. The proportion of real-world steady-state stride speeds within these ranges (gross and net) were identified for all data and for each context. We found that energetically optimal speeds predicted by gross cost of transport were more predictive of walking speeds used during daily life than speeds that would minimize net cost of transport. On average, 82.2% of all steady-state stride speeds were energetically optimal for gross cost of transport for all contexts and participants, while only 45.6% were energetically optimal for net cost of transport. These results suggest that while energetic cost is a factor considered by humans when selecting gait speed in daily life, it is not the sole determining factor. Context contributes to the observed variability in movement parameters both within and between individuals.

In-home Gait Abnormality Detection through Footstep-Induced Floor Vibration Sensing and Person-Invariant Contrastive Learning.

Detecting gait abnormalities is crucial for assessing fall risks and early identification of neuromusculoskeletal disorders such as Parkinson's and stroke. Traditional assessments in gait clinics are infrequent and pose barriers, particularly for disadvantaged populations. Previous efforts have explored sensor-based approaches for in-home gait assessments, yet they face limitations such as visual obstructions (cameras), limited coverage (pressure mats), and the need for device carrying (wearables and insoles). To overcome these limitations, we introduce an in-home gait abnormality detection system using footstep-induced floor vibrations, enabling low-cost, non-intrusive, device-free gait health monitoring. The main research challenge is the high uncertainty in floor vibrations due to gait variations among people, making it challenging to develop a generalizable model for new patients. To address this, we analyze time-frequency-domain features of floor vibration data during specific gait phases and develop a feature transformation method through contrastive learning to address the between-people gait variation challenge. Our method transforms the features from vibrations to an embedding space where samples from different people stay close to each other (robust to people variation) while normal and abnormal gait samples are far apart (sensitive to gait abnormality). After that, gait abnormalities are detected by a downstream classifier after feature transformation. We evaluated our approach through a real-world walking experiment with 21 participants and achieved an 85% to 95% mean accuracy in detecting various gait abnormalities. This novel method overcomes prior limitations in in-home gait assessments, offering accessible gait abnormality detection without the need for intrusive devices or labels for new patients.

Wearable Sensors in Other Medical Domains with Application Potential for Orthopedic Trauma Surgery-A Narrative Review.

The use of wearable technology is steadily increasing. In orthopedic trauma surgery, where the musculoskeletal system is directly affected, focus has been directed towards assessing aspects of physical functioning, activity behavior, and mobility/disability. This includes sensors and algorithms to monitor real-world walking speed, daily step counts, ground reaction forces, or range of motion. Several specific reviews have focused on this domain. In other medical fields, wearable sensors and algorithms to monitor digital biometrics have been used with a focus on domain-specific health aspects such as heart rate, sleep, blood oxygen saturation, or fall risk. This review explores the most common clinical and research use cases of wearable sensors in other medical domains and, from it, derives suggestions for the meaningful transfer and application in an orthopedic trauma context.

Ubiquitous Gait Analysis through Footstep-Induced Floor Vibrations.

Quantitative analysis of human gait is critical for the early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson's disease, stroke, and cerebral palsy. Gait analysis typically involves estimating gait characteristics, such as spatiotemporal gait parameters and gait health indicators (e.g., step time, length, symmetry, and balance). Traditional methods of gait analysis involve the use of cameras, wearables, and force plates but are limited in operational requirements when applied in daily life, such as direct line-of-sight, carrying devices, and dense deployment. This paper introduces a novel approach for gait analysis by passively sensing floor vibrations generated by human footsteps using vibration sensors mounted on the floor surface. Our approach is low-cost, non-intrusive, and perceived as privacy-friendly, making it suitable for continuous gait health monitoring in daily life. Our algorithm estimates various gait parameters that are used as standard metrics in medical practices, including temporal parameters (step time, stride time, stance time, swing time, double-support time, and single-support time), spatial parameters (step length, width, angle, and stride length), and extracts gait health indicators (cadence/walking speed, left-right symmetry, gait balance, and initial contact types). The main challenge we addressed in this paper is the effect of different floor types on the resultant vibrations. We develop floor-adaptive algorithms to extract features that are generalizable to various practical settings, including homes, hospitals, and eldercare facilities. We evaluate our approach through real-world walking experiments with 20 adults with 12,231 labeled gait cycles across concrete and wooden floors. Our results show 90.5% (RMSE 0.08s), 71.3% (RMSE 0.38m), and 92.3% (RMSPE 7.7%) accuracy in estimating temporal, spatial parameters, and gait health indicators, respectively.

Patient Focus: Moving Beyond Step Count: An Explanation of Real-World Walking Behaviors Are Associated With Early-Stage Heart Failure: A Project Baseline Health Study

In this issue of the Journal of Cardiac Failure, Dr. Sooyoon Shin and colleagues look at whether information from wearable devices, or activity trackers, could improve how a medical team monitors activity limitations for people who are at risk of developing heart failure symptoms.

Real-world walking behaviors are associated with early-stage heart failure: a Project Baseline Health Study

BackgroundData collected via wearables may complement in-clinic assessments to monitor subclinical heart failure (HF). ObjectivesEvaluate the association of sensor-based digital walking measures with HF stage and characterize their correlation with in-clinic measures of physical performance, cardiac function and participant reported outcomes (PROs) in individuals with early HF. MethodsThe analyzable cohort included participants from the Project Baseline Health Study (PBHS) with HF stage 0, A, or B, or adaptive remodeling phenotype (without risk factors but with mild echocardiographic change, termed RF-/ECHO+) (based on available first-visit in-clinic test and echocardiogram results) and with sufficient sensor data. We computed daily values per participant for 18 digital walking measures, comparing HF subgroups vs stage 0 using multinomial logistic regression and characterizing associations with in-clinic measures and PROs with Spearman's correlation coefficients, adjusting all analyses for confounders. ResultsIn the analyzable cohort (N=1265; 50.6% of the PBHS cohort), one standard deviation decreases in 17/18 walking measures were associated with greater likelihood for stage-B HF (multivariable-adjusted odds ratios [ORs] vs stage 0 ranging from 1.18-2.10), or A (ORs vs stage 0, 1.07-1.45), and lower likelihood for RF-/ECHO+ (ORs vs stage 0, 0.80-0.93). Peak 30-minute pace demonstrated the strongest associations with stage B (OR vs stage 0=2.10; 95% CI:1.74-2.53) and A (OR vs stage 0=1.43; 95% CI:1.23-1.66). Decreases in 13/18 measures were associated with greater likelihood for stage-B HF vs stage A. Strength of correlation with physical performance tests, echocardiographic cardiac-remodeling and dysfunction indices and PROs was greatest in stage B, then A, and lowest for 0. ConclusionsDigital measures of walking captured by wearable sensors could complement clinic-based testing to identify and monitor pre-symptomatic HF.

도시의 공간 패턴 분석을 통한 메타버스 기반 근접성 향상 실용화 방안 연구

This study proposes a method for addressing the limitations of physical proximity in real-world walking environments and focusing on Metaverse-based solutions. The first phase of the research entailed a community analysis centered on locations in Daejeon, through which problematic areas were identified and defined. The second phase involved conducting a survey among users of the Metaverse environment to explore methods of enhancing proximity. Based on these findings, the study developed three approaches to improve proximity in a Metaverse-based environment. It emphasizes the importance of objective data analysis and precise problem definition, underscoring the necessity of developing and designing solutions within the Metaverse environment. Moreover, the study suggests that to enhance user experiences in the Metaverse, a thorough analysis of regional characteristics is essential, followed by the development of tailored Metaverse solutions.

Validity of iPhone health application step count in semi free-living conditions

The iPhone’s validity for measuring steps has been mainly investigated under laboratory conditions, while studies that include real-world conditions are still scarce. We examined the validity of iPhones in measuring steps in real-world walking conditions, while using direct observation with video as reference. A sample of 100 adults who owned an iPhone 5S or higher was included and participants were randomly allocated to one of two protocols. Limits of Agreement (LoA), Mean Absolute Percentage Error (MAPE), linear-regression and Bland-Altman analyses were carried out. In Protocol-1, which includes straight-line and zigzag conditions, we observed a low MAPE lower than 4%. Bland-Altman analyses assessed a high accordance of approximately 6 steps under both conditions. Differences between the iPhone and direct observation were only noticeable in straight-line condition (p = 0.002). Likewise, protocol-2 (three 50-step conditions: an upward 5 % slope, a flat surface, and a downward -5% slope) showed low MAPE values (3.78 %) for the upward slope, 2.41 % for the downward slope, and 2.37 % in absence of slope), and differences between the iPhone and direct observation were only observed for the downward slope (p = 0.017), with the iPhone overestimating. Our findings revealed that iPhone might be a reliable tool for monitoring walking in real-life conditions, however, downward slope seems to generate overestimation, which deserves future investigation.

Classification of human walking context using a single-point accelerometer

Real-world walking data offers rich insights into a person’s mobility. Yet, daily life variations can alter these patterns, making the data challenging to interpret. As such, it is essential to integrate context for the extraction of meaningful information from real-world movement data. In this work, we leveraged the relationship between the characteristics of a walking bout and context to build a classification algorithm to distinguish between indoor and outdoor walks. We used data from 20 participants wearing an accelerometer on the thigh over a week. Their walking bouts were isolated and labeled using GPS and self-reporting data. We trained and validated two machine learning models, random forest and ensemble Support Vector Machine, using a leave-one-participant-out validation scheme on 15 subjects. The 5 remaining subjects were used as a testing set to choose a final model. The chosen model achieved an accuracy of 0.941, an F1-score of 0.963, and an AUROC of 0.931. This validated model was then used to label the walks from a different dataset with 15 participants wearing the same accelerometer. Finally, we characterized the differences between indoor and outdoor walks using the ensemble of the data. We found that participants walked significantly faster, longer, and more continuously when walking outdoors compared to indoors. These results demonstrate how movement data alone can be used to obtain accurate information on important contextual factors. These factors can then be leveraged to enhance our understanding and interpretation of real-world movement data, providing deeper insights into a person’s health.

Real-world walking cadence in people with COPD.

The clinical validity of real-world walking cadence in people with COPD is unsettled. Our objective was to assess the levels, variability and association with clinically relevant COPD characteristics and outcomes of real-world walking cadence. We assessed walking cadence (steps per minute during walking bouts longer than 10 s) from 7 days' accelerometer data in 593 individuals with COPD from five European countries, and clinical and functional characteristics from validated questionnaires and standardised tests. Severe exacerbations during a 12-month follow-up were recorded from patient reports and medical registries. Participants were mostly male (80%) and had mean±sd age of 68±8 years, post-bronchodilator forced expiratory volume in 1 s (FEV1) of 57±19% predicted and walked 6880±3926 steps·day-1. Mean walking cadence was 88±9 steps·min-1, followed a normal distribution and was highly stable within-person (intraclass correlation coefficient 0.92, 95% CI 0.90-0.93). After adjusting for age, sex, height and number of walking bouts in fractional polynomial or linear regressions, walking cadence was positively associated with FEV1, 6-min walk distance, physical activity (steps·day-1, time in moderate-to-vigorous physical activity, vector magnitude units, walking time, intensity during locomotion), physical activity experience and health-related quality of life and negatively associated with breathlessness and depression (all p<0.05). These associations remained after further adjustment for daily steps. In negative binomial regression adjusted for multiple confounders, walking cadence related to lower number of severe exacerbations during follow-up (incidence rate ratio 0.94 per step·min-1, 95% CI 0.91-0.99, p=0.009). Higher real-world walking cadence is associated with better COPD status and lower severe exacerbations risk, which makes it attractive as a future prognostic marker and clinical outcome.

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 (2.5h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5h/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 0.12m/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.

Changes in real-world walking speed following 60-day bed-rest

The aim of this work was to explore whether real-world walking speed (RWS) would change as a consequence of 60-day bed-rest. The main hypothesis was that daily RWS would decrease after the bed-rest, with a subsequent recovery during the first days of re-ambulation. Moreover, an exploratory analysis was done in order to understand whether there is an agreement between the loss in RWS after bed-rest and the loss in the maximum oxygen uptake capacity (VO2max), or the loss in maximal vertical jump power (JUMP) respectively. Twenty-four subjects were randomly assigned to one of three groups: a continuous artificial gravity group, an intermittent artificial gravity group, or a control group. The fitted linear mixed effects model showed a significant decrease (p < 0.001) of RWS after the 60-day bed-rest and a subsequent increase (p < 0.001) of RWS during the 14-day recovery period in the study facility. No or little agreement was found between the loss in RWS and the loss in VO2max capacity or the loss in maximal vertical jumping power (RWS vs. VO2max: p = 0.81, RWS vs. JUMP: p = 0.173). Decreased RWS after bed-rest, with a follow-up recovery was observed for all three groups, regardless of the training intervention. This suggests that RWS, also in these settings, was able to reflect a de-conditioning and follow-up recovery process.

Foot orientation and trajectory variability in locomotion: Effects of real-world terrain.

Capturing human locomotion in nearly any environment or context is becoming increasingly feasible with wearable sensors, giving access to commonly encountered walking conditions. While important in expanding our understanding of locomotor biomechanics, these more variable environments present challenges to identify changes in data due to person-level factors among the varying environment-level factors. Our study examined foot-specific biomechanics while walking on terrain commonly encountered with the goal of understanding the extent to which these variables change due to terrain. We recruited healthy adults to walk at self-selected speeds on stairs, flat ground, and both shallow and steep sloped terrain. A pair of inertial measurement units were embedded in both shoes to capture foot biomechanics while walking. Foot orientation was calculated using a strapdown procedure and foot trajectory was determined by double integrating the linear acceleration. Stance time, swing time, cadence, sagittal and frontal orientations, stride length and width were extracted as discrete variables. These data were compared within-participant and across terrain conditions. The physical constraints of the stairs resulted in shorter stride lengths, less time spent in swing, toe-first foot contact, and higher variability during stair ascent specifically (p<0.05). Stride lengths increased when ascending compared to descending slopes, and the sagittal foot angle at initial contact was greatest in the steep slope descent condition (p<0.05). No differences were found between conditions for horizontal foot angle in midstance (p≥0.067). Our results show that walking on slopes creates differential changes in foot biomechanics depending on whether one is descending or ascending, and stairs require different biomechanics and gait timing than slopes or flat ground. This may be an important factor to consider when making comparisons of real-world walking bouts, as greater proportions of one terrain feature in a data set could create bias in the outcomes. Classifying terrain in unsupervised walking datasets would be helpful to avoid comparing metrics from different walking terrain scenarios.

PropelWalker: A Leg-Based Wearable System With Propeller-Based Force Feedback for Walking in Fluids in VR.

There has been an increasing focus on haptic interfaces for virtual reality (VR), to support a high-quality touch experience. However, it is still challenging to haptically simulate the real-world walking experience in different fluid mediums. To tackle this problem, we present PropelWalker, a pair of calf-worn haptic devices for simulating the buoyancy and the resistant force when the human's lower limbs are interacting with different fluids and materials in VR. By using four ducted fans, two installed on each calf, the system can control the strength and the direction of the airflow in real time to provide different levels of force. Our technical evaluation shows that PropelWalker can generate vertical forces up to 27N in two directions (i.e., upward and downward) within 0.85 seconds. Furthermore, the system can stably maintain the generated force with minor turbulence. We further conducted three user-perception studies to understand the capability of PropelWalker to generate distinguishable force stimuli. First, we conducted the just-noticeable-difference (JND) experiments to investigate the threshold of the human perception of on-leg air-flow force feedback. Our second perception study showed that users could distinguish four PropelWalker-generated force levels for simulating different walking mediums (i.e., dry ground, water, mud, and sand), with an average accuracy of 94.2%. Lastly, our VR user study showed that PropelWalker could significantly improve the users' sense of presence in VR.

Walking Bout Detection for People Living in Long Residential Care: A Computationally Efficient Algorithm for a 3-Axis Accelerometer on the Lower Back.

Accurate and reliable measurement of real-world walking activity is clinically relevant, particularly for people with mobility difficulties. Insights on walking can help understand mobility function, disease progression, and fall risks. People living in long-term residential care environments have heterogeneous and often pathological walking patterns, making it difficult for conventional algorithms paired with wearable sensors to detect their walking activity. We designed two walking bout detection algorithms for people living in long-term residential care. Both algorithms used thresholds on the magnitude of acceleration from a 3-axis accelerometer on the lower back to classify data as "walking" or "non-walking". One algorithm had generic thresholds, whereas the other used personalized thresholds. To validate and evaluate the algorithms, we compared the classifications of walking/non-walking from our algorithms to the real-time research assistant annotated labels and the classification output from an algorithm validated on a healthy population. Both the generic and personalized algorithms had acceptable accuracy (0.83 and 0.82, respectively). The personalized algorithm showed the highest specificity (0.84) of all tested algorithms, meaning it was the best suited to determine input data for gait characteristic extraction. The developed algorithms were almost 60% quicker than the previously developed algorithms, suggesting they are adaptable for real-time processing.