Technological Solutions for Human Movement Analysis in Obese Subjects: A Systematic Review

Freezing of gait (FoG) is a disabling clinical phenomenon of Parkinson’s disease (PD) characterized by the inability to move the feet forward despite the intention to walk. It is one of the most troublesome symptoms of PD, leading to an increased risk of falls and reduced quality of life. The combination of wearable inertial sensors and machine learning (ML) algorithms represents a feasible solution to monitor FoG in real-world scenarios. However, traditional FoG detection algorithms process all data indiscriminately without considering the context of the activity during which FoG occurs. This study aimed to develop a lightweight, context-aware algorithm that can activate FoG detection systems only under certain circumstances, thus reducing the computational burden. Several approaches were implemented, including ML and deep learning (DL) gait recognition methods, as well as a single-threshold method based on acceleration magnitude. To train and evaluate the context algorithms, data from a single inertial sensor were extracted using three different datasets encompassing a total of eighty-one PD patients. Sensitivity and specificity for gait recognition ranged from 0.95 to 0.96 and 0.80 to 0.93, respectively, with the one-dimensional convolutional neural network providing the best results. The threshold approach performed better than ML- and DL-based methods when evaluating the effect of context awareness on FoG detection performance. Overall, context algorithms allow for discarding more than 55% of non-FoG data and less than 4% of FoG episodes. The results indicate that a context classifier can reduce the computational burden of FoG detection algorithms without significantly affecting the FoG detection rate. Thus, implementation of context awareness can present an energy-efficient solution for long-term FoG monitoring in ambulatory and free-living settings.

Section: Technologies For the Automatic Recognition Of Fogmentioning

confidence: 99%

Context Recognition Algorithms for Energy-Efficient Freezing-of-Gait Detection in Parkinson’s Disease

Borzì

Sigcha

Olmo

2023

“…A broad range of ages are represented in these papers, which suggests that the research conducted is generalisable to a wider sample. The mass of the participants in each sample is infrequently reported, and none of the studies had exclusion criteria relating to mass or BMI, which may suggest that the researchers do not consider this a confounding variable when assessing gait characteristics despite the potential accuracy issues due to soft tissue artefacts [40].…”

Section: Study Characteristicsmentioning

confidence: 99%

A Scoping Review of the Validity and Reliability of Smartphone Accelerometers When Collecting Kinematic Gait Data

Strongman,

Cavallerio,

Timmis

et al. 2023

The aim of this scoping review is to evaluate and summarize the existing literature that considers the validity and/or reliability of smartphone accelerometer applications when compared to ‘gold standard’ kinematic data collection (for example, motion capture). An electronic keyword search was performed on three databases to identify appropriate research. This research was then examined for details of measures and methodology and general study characteristics to identify related themes. No restrictions were placed on the date of publication, type of smartphone, or participant demographics. In total, 21 papers were reviewed to synthesize themes and approaches used and to identify future research priorities. The validity and reliability of smartphone-based accelerometry data have been assessed against motion capture, pressure walkways, and IMUs as ‘gold standard’ technology and they have been found to be accurate and reliable. This suggests that smartphone accelerometers can provide a cheap and accurate alternative to gather kinematic data, which can be used in ecologically valid environments to potentially increase diversity in research participation. However, some studies suggest that body placement may affect the accuracy of the result, and that position data correlate better than actual acceleration values, which should be considered in any future implementation of smartphone technology. Future research comparing different capture frequencies and resulting noise, and different walking surfaces, would be useful.

“…Motor assessment in obese has been primarily focused on gait analysis performed with marker-based optoelectronic motion capture (MoCap) systems within a controlled laboratory set-up [15]. Differences in terms of spatio-temporal parameters between obese and normal-weight individuals have been found, even though results are often inconsistent among different studies, mainly due to the use of dissimilar experimental protocols and the subjects' characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…A 200 m straight walking was performed, and sensors were placed on the sacrum and, bilaterally, on the front of the thighs, the shanks, and the dorsal surface of the feet. On the other hand, there is little research concerning upper-limb movements in obese individuals [15]. Nevertheless, promising results have been reported by various studies on healthy individuals in estimating upper body joint angles using IMUs.…”

Section: Introductionmentioning

confidence: 99%

Assessment of an IMU-Based Experimental Set-Up for Upper Limb Motion in Obese Subjects

Cerfoglio,

Lopomo,

Capodaglio

et al. 2023

Self Cite

In recent years, wearable systems based on inertial sensors opened new perspectives for functional motor assessment with respect to the gold standard motion capture systems. The aim of this study was to validate an experimental set-up based on 17 body-worn inertial sensors (Awinda, Xsens, The Netherlands), addressing specific body segments with respect to the state-of-the art system (VICON, Oxford Metrics Ltd., Oxford, UK) to assess upper limb kinematics in obese, with respect to healthy subjects. Twenty-three obese and thirty healthy weight individuals were simultaneously acquainted with the two systems across a set of three tasks for upper limbs (i.e., frontal arm rise, lateral arm rise, and reaching). Root Mean Square error (RMSE) was computed to quantify the differences between the measurements provided by the systems in terms of range of motion (ROM), whilst their agreement was assessed via Pearson’s correlation coefficient (PCC) and Bland–Altman (BA) plots. In addition, the signal waveforms were compared via one-dimensional statistical parametrical mapping (SPM) based on a paired t-test and a two-way ANOVA was applied on ROMs. The overall results partially confirmed the correlation and the agreement between the two systems, reporting only a moderate correlation for shoulder principal rotation angle in each task (r~0.40) and for elbow/flexion extension in obese subjects (r = 0.66), whilst no correlation was found for most non-principal rotation angles (r < 0.40). Across the performed tasks, an average RMSE of 34° and 26° was reported in obese and healthy controls, respectively. At the current state, the presence of bias limits the applicability of the inertial-based system in clinics; further research is intended in this context.