Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization

Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual’s mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the scientific community. We also provide samples of standardized data, so that both the structure of the data and the procedure can be easily understood and reproduced

An objective methodology for the selection of a device for continuous mobility assessment

Continuous monitoring by wearable technology is ideal for quantifying mobility outcomes in “real-world” conditions. Concurrent factors such as validity, usability, and acceptability of such technology need to be accounted for when choosing a monitoring device. This study proposes a bespoke methodology focused on defining a decision matrix to allow for effective decision making. A weighting system based on responses (n = 69) from a purpose-built questionnaire circulated within the IMI Mobilise-D consortium and its external collaborators was established, accounting for respondents’ background and level of expertise in using wearables in clinical practice. Four domains (concurrent validity, CV; human factors, HF; wearability and usability, WU; and data capture process, CP), associated evaluation criteria, and scores were established through literature research and group discussions. While the CV was perceived as the most relevant domain (37%), the others were also considered highly relevant (WU: 30%, HF: 17%, CP: 16%). Respondents (~90%) preferred a hidden fixation and identified the lower back as an ideal sensor location for mobility outcomes. Overall, this study provides a novel, holistic, objective, as well as a standardized approach accounting for complementary aspects that should be considered by professionals and researchers when selecting a solution for continuous mobility monitoring

Instrumented gait assessment with a single wearable: an introductory tutorial

Gait is a powerful tool to identify ageing and track disease progression. Yet, its measurement via traditional instrumentations remains restricted to the laboratory or bespoke clinical facilities. The potential for that to change is due to the advances in wearable technology where the synergy between accelerometer-based body worn monitors and smart algorithms has provided the potential of ‘a gait lab on a chip’. The deployment of wearables can allow the researcher/clinician to continuously assess the participant accurately and robustly over time. Commercially available wearables for gait quantification remain expensive and are restricted to a limited number of characteristics unsuitable for a comprehensive clinical assessment. However, the increasing demand for low cost diagnostics has fuelled the shift in how health-related resources are distributed. As such the interest in open platform technology and novel research methodologies has begun to harmonise engineering solutions with clinical needs. We provide an introduction to conduct an instrumented gait assessment with a discrete, low cost, accelerometer-based body worn monitor. We show that the capture and interpretation of raw gait signals with a common scripting language (MATLAB®) can be straightforward. In addition, we highlight best approaches and hope that this will help compliment any analytical tool-kit as part of any modern clinical assessment

Consensus based framework for digital mobility monitoring

Digital mobility assessment using wearable sensor systems has the potential to capture walking performance in a patient's natural environment. It enables monitoring of health status and disease progression and evaluation of interventions in real-world situations. In contrast to laboratory settings, real-world walking occurs in non-conventional environments and under unconstrained and uncontrolled conditions. Despite the general understanding, there is a lack of agreed definitions about what constitutes real-world walking, impeding the comparison and interpretation of the acquired data across systems and studies. The goal of this study was to obtain expert-based consensus on specific aspects of real-world walking and to provide respective definitions in a common terminological framework. An adapted Delphi method was used to obtain agreed definitions related to real-world walking. In an online survey, 162 participants from a panel of academic, clinical and industrial experts with experience in the field of gait analysis were asked for agreement on previously specified definitions. Descriptive statistics was used to evaluate whether consent (> 75% agreement as defined a priori) was reached. Of 162 experts invited to participate, 51 completed all rounds (31.5% response rate). We obtained consensus on all definitions ("Walking"> 90%, "Purposeful"> 75%, "Real-world"> 90%, "Walking bout"> 80%, "Walking speed"> 75%, "Turning"> 90% agreement) after two rounds. The identification of a consented set of realworld walking definitions has important implications for the development of assessment and analysis protocols, as well as for the reporting and comparison of digital mobility outcomes across studies and systems. The definitions will serve as a common framework for implementing digital and mobile technologies for gait assessment and are an important link for the transition from supervised to unsupervised gait assessment

Estimating cut points: A simple method for new wearables

Wearable technology is readily available for continuous assessment due to a growing number of commercial devices with increased data capture capabilities. However, many commercial devices fail to support suitable parameters (cut points) derived from the literature to help quantify physical activity (PA) due to differences in manufacturing. A simple metric to estimate cut points for new wearables is needed to aid data analysis. Objective: The purpose of this pilot study was to investigate a simple methodology to determine cut points based on ratios between sedentary behaviour (SB) and PA intensities for a new wrist worn device (PRO-Diary™) by comparing its output to a validated and well characterised ‘gold standard’ (ActiGraph™). Study design: Twelve participants completed a semi-structured (four-phase) treadmill protocol encompassing SB and three PA intensity levels (light, moderate, vigorous). The outputs of the devices were compared accounting for relative intensity. Results: Count ratios (6.31, 7.68, 4.63, 3.96) were calculated to successfully determine cut-points for the new wrist worn wearable technology during SB (0–426) as well as light (427–803), moderate (804–2085) and vigorous (≥2086) activities, respectively. Conclusion: Our findings should be utilised as a primary reference for investigations seeking to use new (wrist worn) wearable technology similar to that used here (i.e., PRO-Diary™) for the purposes of quantifying SB and PA intensities. The utility of count ratios may be useful in comparing devices or SB/PA values estimated across different studies. However, a more robust examination is required for different devices, attachment locations and on larger/diverse cohorts

A robust walking detection algorithm using a single foot-worn inertial sensor: validation in real-life settings

Walking activity and gait parameters are considered among the most relevant mobility-related parameters. Currently, gait assessments have been mainly analyzed in laboratory or hospital settings, which only partially reflect usual performance (i.e., real world behavior). In this study, we aim to validate a robust walking detection algorithm using a single foot-worn inertial measurement unit (IMU) in real-life settings. We used a challenging dataset including 18 individuals performing free-living activities. A multi-sensor wearable system including pressure insoles, multiple IMUs, and infrared distance sensors (INDIP) was used as reference. Accurate walking detection was obtained, with sensitivity and specificity of 98 and 91% respectively. As robust walking detection is needed for ambulatory monitoring to complete the processing pipeline from raw recorded data to walking/mobility outcomes, a validated algorithm would pave the way for assessing patient performance and gait quality in real-world conditions