Introduction Conventional clinical assessments do not fully capture how Parkinson's disease (PD) affects mobility in daily life. Integrating digital mobility outcomes (DMOs) from wearable devices with GPS-derived contextual data could provide richer insight into real-world mobility, yet this approach remains largely unexplored. Similarly, data-driven modeling of DMO distributions, such as walking speed, may reveal clinically relevant changes in mobility that are obscured by averaged measures. This study (i) examined how indoor-outdoor context enhances interpretation of real-world mobility, and (ii) applied Gaussian Mixture Modeling (GMM) to characterize data-driven patterns within walking speed distributions in people with PD.Methods Fifty-two people with PD (PwP) and 19 older adult controls were recruited from the CiC and Mobilise-D studies. DMOs were estimated from a single wearable device, and indoor-outdoor location was synchronized with GPS data from a smartphone. GMM was applied to estimate the optimal number of walking speed modes. Generalized linear models compared DMOs between indoor and outdoor contexts and between cohorts, adjusting for age and sex.Results Thirty-nine PwP and 17 controls had valid contextual data. Both cohorts performed significantly more indoor than outdoor walking bouts, with longer walking durations outdoors. Only controls walked significantly slower and with shorter strides indoors versus outdoors, while both groups showed longer stride duration indoors. Between-cohort differences emerged only outdoors, with PwP exhibiting higher cadence. Most participants across both cohorts displayed three walking speed modes, which were associated with medication dosage and motor severity.Discussion This study demonstrates the potential of GPS-derived contextual information to enhance interpretation of real-world mobility outcomes in PD. Walking speed modes show promise for capturing novel clinical insight, though further technical and clinical validation is required to establish their robustness and clinical relevance.

Objective, continuous assessment of real-world mobility using wearables has significant potential to transform clinical research and practice, yet the field lacks standardised, open-source tools that enable reproducible algorithm real-world validation, across multiple clinical cohorts. This would improve transparency around definitions and performance, thereby enhancing interpretation and more meaningful comparison across studies. The Mobilise-D consortium validated a comprehensive analytical pipeline for estimating digital mobility outcomes from wearables, originally implemented in a combination of MATLAB, R, and Python codes. To overcome the licencing, reproducibility, and accessibility limitations of this implementation, the pipeline has been re-implemented and re-validated, against gold standards, as the open-source mobgap Python package. Here, we describe the mobgap ecosystem, detail how algorithms can be integrated and benchmarked in a reproducible way and present a re-validation of the pipeline against reference data across six clinical cohorts under real-world conditions. Validation results showed that across all cohorts, walking speed was estimated with an absolute error of 0.10 m/s and an intraclass correlation coefficient (ICC) of 0.81, demonstrating comparable or superior performance to the original implementation. Mobgap (v1.2) is openly available and is intended to serve as a reproducible reference implementation and benchmarking platform for researchers developing or validating mobility analysis algorithms using wearable data.

A key challenge in smartphone-based assessment of motor capacity is that patients may wear their smartphone in varying positions, while state-of-the-art algorithms have not been designed and validated for multiple device locations. This paper proposes a solution to estimating foot-to-ground initial contacts (ICs) during gait using inertial measurement unit (IMU) sensor data collected from a smartphone agnostic of its location in a cloth front or back pocket. FAIR-Q, an algorithm originally validated for data collected from the lower trunk was further tuned for this intended use, and tested on IMU data collected in cloth pocket positions from both healthy adults (n = 83, age range: 20–83 y.o.) and people with Multiple Sclerosis (n = 50, age range: 22–61 y.o., EDSS score: 0–6) during a 30s walk test. The performance of FAIR-Q was compared against a gold standard multi-sensor device in terms of sensitivity and measurement error in identifying ICs and measuring step and stride durations. Excellent performance was achieved for both groups in all tested conditions (test-level relative errors for duration measures < 1

The L-test is a performance-based measure to assess balance and mobility. Currently, the primary outcome from this test is the time required to finish it. In this study we present the instrumented L-test (iL-test), an L-test wherein mobility is evaluated by means of a wearable inertial sensor worn at the lower back. We analyzed data from 113 people across seven cohorts: healthy adults, chronic obstructive pulmonary disease, multiple sclerosis, congestive heart failure, Parkinson’s disease, proximal femoral fracture, and transfemoral amputation. The iL-test automatic segmentation was validated using stereophotogrammetry. Univariate and multivariate analyses were performed on 164 kinematic features derived from inertial signals to identify distinct patterns across different cohorts. The iL-test accurately recognized and segmented activities during the L-test for all cohorts (technical validity). A random forest classifier revealed that proximal femoral fracture and transfemoral amputation induced significantly different mobility patterns compared to healthy people with AUC values of 0.89 and 0.99, respectively. Strong correlations were found between kinematic features and clinical scores in multiple sclerosis, congestive heart failure, proximal femoral fracture, and transfemoral amputation, with consistent patterns of decreased movement ranges and smoothness with increasing disease severity. Furthermore, features derived from 90° and 180° turns were found to be important contributors to differentiation amongst cohorts, underscoring the need to evaluate different turn degrees and directions. This study emphasizes the iL-test potential to deliver automated mobility assessment across a wide range of clinical conditions, indicating a prospective avenue for improved mobility assessment and, eventually, more informed healthcare interventions.

Head-worn inertial sensors represent a valuable option to characterize gait in real-world conditions, thanks to the integration with glasses and hearing aids. Few methods based on head-worn sensors allow for stride-by-stride gait speed estimation, but none has been developed with data collected in real-world settings. This study aimed at validating a two-steps machine learning method to estimate initial contacts and stride-by-stride speed in real-world gait using a single inertial sensor attached to the temporal region. A convolutional network is used to detect strides. Then, stride-by-stride gait speed is inferred from the detected cycles by a gaussian process regression model. A multi-sensor wearable system was used to label over 100,000 strides recorded from 15 healthy young adults during supervised acquisitions and real-world unsupervised walking. The stride detector achieved high detection rate (F1-score > 92%) and accuracy (mean absolute error < 40 ms). Very strong correlation between target and predicted speed (Spearman coefficient > 0.86) and low mean absolute error (< 0.085 m/s) were observed. The method proved valid for the quantitative evaluation of stride-by-stride gait speed in real-world conditions. These findings lay the technical and analytical groundwork for future clinical validation and application of gait analysis frameworks that integrate inertial sensors with head-worn devices.

BACKGROUND:Mobility assessment is essential for monitoring disease progression in people with multiple sclerosis (PwMS). Technologies such as wearable sensors show potential for this purpose, but consensus is needed to optimize collection and interpretation of digital measures in PwMS. OBJECTIVE:To propose a framework for measuring and interpreting key aspects of impaired gait in PwMS using a smartphone worn at the waist level. METHODS:The framework was developed on the basis of clinical understanding and knowledge of sensor signal processing, supported by a systematic literature review (SLR). The SLR targeted articles published after 2011 that measured gait characteristics in PwMS. Findings were used to propose standardized definitions for complementary gait domains and define digital measures that should be captured for each domain. RESULTS:The resulting framework for PwMS recommends definitions for pace, rhythm, stability, symmetry, variability, smoothness, complexity and fatigability gait domains. For each domain, a set of digital measures is described with respect to their interpretability and associated caveats. CONCLUSION:This framework provides recommendations for measuring complex gait patterns in PwMS using widely available technology. This work promotes the use of standardized gait domain definitions and harmonized descriptions of associated digital measures, paving the way for future validation efforts.

Wearable technology has rapidly advanced, opening new possibilities for context-aware applications in fields such as healthcare and gait analysis, where distinguishing between indoor and outdoor environments is essential. This is often accomplished through technologies like GPS, Wi-Fi, cellular, and Bluetooth which, however, come with privacy concerns, high power consumption, and dependency on external infrastructure. To address these challenges, recent studies have preliminary exploited the ambient magnetic field, though comprehensive validation with real-life data is lacking. This article seeks to validate machine learning techniques, i.e., random forest (RF), extreme gradient boosting, and stacked long short-term memory (LSTM) networks, for indoor-outdoor discrimination using exclusively magnetometer data from the daily activities of 20 participants in four cities across three countries. The study investigated the most effective magnetometer placement (feet, lower back, and nondominant wrist) and pre-processing techniques (e.g., features and window size). Reference data are obtained through a GPS-based algorithm coupled with a geographical database running on a smartphone. The extreme gradient boosting algorithm yielded the best results, with an accuracy of 0.91, an F1 -score of 0.90, and an area under the ROC curve of 0.94. These findings confirm the feasibility of accurately estimating indoor/outdoor context information from a magnetometer at the same update frequency as a common GPS, but with important energy savings. The proposed model can be integrated into state-of-the-art gait analysis systems, being able to discriminate the location to avoid misinterpreting gait deviations in real-world settings, thus supporting continuous and ubiquitous gait monitoring. Datasets and algorithm implementations have been made publicly available.

Reduction in mobility due to gait impairment is a critical consequence of diseases affecting the neuromusculoskeletal system, making detecting anomalies in a person’s gait a key area of interest. This challenge is compounded by within-subject and between-subject variability, further emphasized in individuals with multiple sclerosis (MS), where gait patterns exhibit significant heterogeneity. This study introduces a novel perspective on modeling kinematic gait patterns, recognizing the inherent hierarchical structure of the data, which is gathered from contralateral limbs, individuals, and groups of individuals comprising a population, using wearable sensors. Rather than summarizing features, this approach models the entire gait cycle functionally, including its variation. A Hierarchical Variational Sparse Heteroscedastic Gaussian Process was used to model the shank angular velocity across 28 MS and 28 healthy individuals. The utility of this methodology was underscored by its granular analysis capabilities. This facilitated a range of quantifiable comparisons, spanning from group-level assessments to patient-specific analyses, addressing the complexity of pathological gait patterns and offering a robust methodology for kinematic pattern characterization for large datasets. The group-level analysis highlighted notable differences during the swing phase and towards the end of the stance phase, aligning with previously established literature findings. Moreover, the study identified the heteroscedastic gait pattern variability as a distinguishing feature of MS gait. Additionally, a novel approach for lower limb gait asymmetry quantification has been proposed. The use of probabilistic hierarchical modeling facilitated a better understanding of the impaired gait pattern, while also expressing potential for extrapolation to other pathological conditions affecting gait.

BackgroundWrist-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. ObjectiveThe 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. MethodsParticipants 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. ResultsThe 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. ConclusionsAlgorithms 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 RegistrationISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987 International Registered Report Identifier (IRRID)RR2-10.1136/bmjopen-2021-050785

Lameness is widely regarded as the most prevalent problem affecting equines globally. Much is understood about the adjustment of upper body posture to reduce loading in an affected limb. However, the relationship between lameness and breakover duration, when the distal limb experiences high tensile stresses, remains an underinvestigated area. Thus, this study aimed to investigate breakover duration at walk in a cohort of horses, quantifying the effect of fore- and hindlimb lameness. It was hypothesized that lameness would induce an asymmetry between breakover durations of affected contralateral limb pairs. Breakover durations of sixteen horses (five sound and eleven lame, as presented by owners) were measured using data collected by hoof-mounted gyroscopes. Breakover durations of the limbs of contralateral pairs were compared, and paired Student's t-tests were used to determine whether differences were significant (p < 0.01). A high degree of symmetry was seen in breakover durations of sound horses, with a mean (SD) duration of 168(19)ms and a negligible mean absolute difference (6ms, p = 0.07). In lame horses, breakover durations of lame limbs (167(22)ms) were longer than those of contralateral limbs (146(23)ms, p < 0.001); and breakover durations of the ipsilateral (160(26)ms) and diagonal (162(24)ms) limbs were equivalent and comparable to those of sound limb pairs. These results indicate that where there is lameness present in a contralateral limb pair, there will be a breakdown in the symmetry of breakover duration, with the most severely affected limb having a significantly longer breakover duration than the contralateral. This pattern should be investigated in the future as a marker to indicate lameness.

OBJECTIVE:The quantification of the way an individual walks is key to the understanding of diseases affecting the neuromuscular system. More specifically, to improve diagnostics and treatment plans, there is a continuous interest in quantifying gait consistency, allowing clinicians to distinguish natural variability of the gait patterns from disease progression or treatment effects. To this end, the current article presents a novel objective method for assessing the consistency of an individual's gait, consisting of two major components. METHODS:Firstly, inertial sensor accelerometer data from both shanks and the lower back is used to fit an AutoRegressive with eXogenous input model. The model residuals are then used as a key feature for gait consistency monitoring. Secondly, the non-parametric maximum mean discrepancy hypothesis test is introduced to measure differences in the distributions of the residuals as a measure of gait consistency. As a paradigmatic case, gait consistency was evaluated both in a single walking test and between tests at different time points in healthy individuals and those affected by multiple sclerosis (MS). RESULTS:It was found that MS patients experienced difficulties maintaining a consistent gait, even when the retest was performed one-hour apart and all external factors were controlled. When the retest was performed one-week apart, both healthy and MS individuals displayed inconsistent gait patterns. CONCLUSION:Gait consistency has been successfully quantified for both healthy and MS individuals. SIGNIFICANCE:This newly proposed approach revealed the detrimental effects of varying assessment conditions on gait pattern consistency, indicating potential masking effects at follow-up assessments.

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.

Background: More responsive, reliable, and clinically valid endpoints of disability are essential to reduce size, duration, and burden of clinical trials in adult persons with spinal muscular atrophy (aPwSMA). Objective: The aim is to investigate the feasibility of smartphone-based assessments in aPwSMA and provide evidence on the reliability and construct validity of sensor-derived measures (SDMs) of mobility and manual dexterity collected remotely in aPwSMA. Methods: Data were collected from 59 aPwSMA (23 walkers, 20 sitters and 16 non-sitters) and 30 age-matched healthy controls (HC). SDMs were extracted from five smartphone-based tests capturing mobility and manual dexterity, which were administered in-clinic and remotely in daily life for four weeks. Reliability (Intraclass Correlation Coefficients, ICC) and construct validity (ability to discriminate between HC and aPwSMA and correlations with Revised Upper Limb Module, RULM and Hammersmith Functional Scale- Expanded HFMSE) were quantified for all SDMs. Results: The smartphone-based assessments proved feasible, with 92.1% average adherence in aPwSMA. The SDMs allowed to reliably assess both mobility and dexterity (ICC > 0.75 for 14/22 SDMs). Twenty-one out of 22 SDMs significantly discriminated between HC and aPwSMA. The highest correlations with the RULM were observed for SDMs from the manual dexterity tests in both non-sitters (Typing, rho = 0.78) and sitters (Pinching, rho = 0.75). In walkers, the highest correlation was between mobility tests and HFMSE (5 U-Turns, rho = 0.79). Conclusions: This exploratory study provides preliminary evidence for the usability of smartphone-based assessments of mobility and manual dexterity in aPwSMA when deployed remotely in participants' daily life. Reliability and construct validity of SDMs remotely collected in real-life was demonstrated, which is a pre-requisite for their use in longitudinal trials. Additionally, three novel smartphone-based performance outcome assessments were successfully established for aPwSMA. Upon further validation of responsiveness to interventions, this technology holds potential to increase the efficiency of clinical trials in aPwSMA.

BackgroundReal-world monitoring using wearable sensors has enormous potential for assessing disease severity and symptoms among persons with Parkinson's disease (PD). Many distinct features can be extracted, reflecting multiple mobility domains. However, it is unclear which digital measures are related to PD severity and are sensitive to disease progression.ObjectivesThe aim was to identify real-world mobility measures that reflect PD severity and show discriminant ability and sensitivity to disease progression, compared to the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scale.MethodsMulticenter real-world continuous (24/7) digital mobility data from 587 persons with PD and 68 matched healthy controls were collected using an accelerometer adhered to the lower back. Machine learning feature selection and regression algorithms evaluated associations of the digital measures using the MDS-UPDRS (I-III). Binary logistic regression assessed discriminatory value using controls, and longitudinal observational data from a subgroup (n = 33) evaluated sensitivity to change over time.ResultsDigital measures were only moderately correlated with the MDS-UPDRS (part II-r = 0.60 and parts I and III-r = 0.50). Most associated measures reflected activity quantity and distribution patterns. A model with 14 digital measures accurately distinguished recently diagnosed persons with PD from healthy controls (81.1%, area under the curve: 0.87); digital measures showed larger effect sizes (Cohen's d: [0.19-0.66]), for change over time than any of the MDS-UPDRS parts (Cohen's d: [0.04-0.12]).ConclusionsReal-world mobility measures are moderately associated with clinical assessments, suggesting that they capture different aspects of motor capacity and function. Digital mobility measures are sensitive to early-stage disease and to disease progression, to a larger degree than conventional clinical assessments, demonstrating their utility, primarily for clinical trials but ultimately also for clinical care. (c) 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

BackgroundSmartphones and wearables are revolutionizing the assessment of cognitive and motor function in neurological disorders, allowing for objective, frequent, and remote data collection. However, these assessments typically provide a plethora of sensor-derived measures (SDMs), and selecting the most suitable measure for a given context of use is a challenging, often overlooked problem. ObjectiveThis analysis aims to develop and apply an SDM selection framework, including automated data quality checks and the evaluation of statistical properties, to identify robust SDMs that describe the cognitive and motor function of people with multiple sclerosis (MS). MethodsThe proposed framework was applied to data from a cross-sectional study involving 85 people with MS and 68 healthy participants who underwent in-clinic supervised and remote unsupervised smartphone-based assessments. The assessment provided high-quality recordings from cognitive, manual dexterity, and mobility tests, from which 47 SDMs, based on established literature, were extracted using previously developed and publicly available algorithms. These SDMs were first separately and then jointly screened for bias and normality by 2 expert assessors. Selected SDMs were then analyzed to establish their reliability, using an intraclass correlation coefficient and minimal detectable change at 95% CI. The convergence of selected SDMs with in-clinic MS functional measures and patient-reported outcomes was also evaluated. ResultsA total of 16 (34%) of the 47 SDMs passed the selection framework. All selected SDMs demonstrated moderate-to-good reliability in remote settings (intraclass correlation coefficient 0.5-0.85; minimal detectable change at 95% CI 19%-35%). Selected SDMs extracted from the smartphone-based cognitive test demonstrated good-to-excellent correlation (Spearman correlation coefficient, |ρ|>0.75) with the in-clinic Symbol Digit Modalities Test and fair correlation with Expanded Disability Status Scale (EDSS) scores (0.25≤|ρ|<0.5). SDMs extracted from the manual dexterity tests showed either fair correlation (0.25≤|ρ|<0.5) or were not correlated (|ρ|<0.25) with the in-clinic 9-hole peg test and EDSS scores. Most selected SDMs from mobility tests showed fair correlation with the in-clinic timed 25-foot walk test and fair to moderate-to-good correlation (0.5<|ρ|≤0.75) with EDSS scores. SDM correlations with relevant patient-reported outcomes varied by functional domain, ranging from not correlated (cognitive test SDMs) to good-to-excellent correlation (|ρ|>0.75) for mobility test SDMs. Overall, correlations were similar when smartphone-based tests were performed in a clinic or remotely. ConclusionsReported results highlight that smartphone-based assessments are suitable tools to remotely obtain high-quality SDMs of cognitive and motor function in people with MS. The presented SDM selection framework promises to increase the interpretability and standardization of smartphone-based SDMs in people with MS, paving the way for their future use in interventional trials.

Individual muscle segmentation is the process of partitioning medical images into regions representing each muscle. It can be used to isolate spatially structured quantitative muscle characteristics, such as volume, geometry, and the level of fat infiltration. These features are pivotal to measuring the state of muscle functional health and in tracking the response of the body to musculoskeletal and neuromusculoskeletal disorders. The gold standard approach to perform muscle segmentation requires manual processing of large numbers of images and is associated with significant operator repeatability issues and high time requirements. Deep learning-based techniques have been recently suggested to be capable of automating the process, which would catalyse research into the effects of musculoskeletal disorders on the muscular system. In this study, three convolutional neural networks were explored in their capacity to automatically segment twenty-three lower limb muscles from the hips, thigh, and calves from magnetic resonance images. The three neural networks (UNet, Attention UNet, and a novel Spatial Channel UNet) were trained independently with augmented images to segment 6 subjects and were able to segment the muscles with an average Relative Volume Error (RVE) between -8.6% and 2.9%, average Dice Similarity Coefficient (DSC) between 0.70 and 0.84, and average Hausdorff Distance (HD) between 12.2 and 46.5 mm, with performance dependent on both the subject and the network used. The trained convolutional neural networks designed, and data used in this study are openly available for use, either through re-training for other medical images, or application to automatically segment new T1-weighted lower limb magnetic resonance images captured with similar acquisition parameters.

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.