Wireless Inertial Measurement Unit Research Articles

Differentiating Postural and Kinetic Tremor Responses to Deep Brain Stimulation in Essential Tremor.

While deep brain stimulation (DBS) targeting the ventral intermediate nucleus (VIM) of thalamus or posterior subthalamic area (PSA) can suppress forms of action tremor in people with Essential Tremor, previous studies have suggested postural tremor may respond more robustly than kinetic tremor to DBS. In this study, we aimed to more precisely quantify the (1) onset/offset dynamics and (2) steady-state effects of VIM/PSA-DBS on postural and kinetic tremor. Tremor data from wireless inertial measurement units were collected from 11 participants with ET (20 unilaterally assessed DBS leads). Three postural hold tasks and one kinetic task were performed with stimulation turned off, in 2-min intervals after enabling unilateral DBS at the clinician-optimized DBS setting (15 min), and in 2-min intervals following cessation of DBS (5 min). At baseline, kinetic tremor had significantly higher amplitudes, standard deviation, and frequency than postural tremor (P < 0.001). DBS had a more robust acute effect on postural tremors (54% decrease, P < 0.001), with near immediate tremor suppression in amplitude and standard deviation, but had non-significant improvement of kinetic tremor on the population-level across the wash-in period (34% decrease). Tremor response was not equivalent between wash-in and wash-out timepoints and involved substantial individual variability including task-specific rebound or long wash-out effects. Programming strategies for VIM/PSA-DBS should consider the individual temporal and effect size variability in postural versus kinetic tremor improvement. Improved targeting and programming strategies around VIM and PSA may be necessary to equivalently suppress both postural and kinetic tremors.

Real-Time IMU-Based Kinematics in the Presence of Wireless Data Drop.

Wireless inertial motion capture holds promise for real-time human-machine interfaces and home-based rehabilitation applications. However, wireless data drop can cause significant estimation errors deteriorating performance or even making the system unusable. It is currently unclear how to estimate non-periodic kinematics with wearable inertial measurement units (IMUs) in the presence of wireless data drop (packet loss). We thus propose a novel inference encoder-decoder network model for real-time kinematics during dynamic movement. Twenty-four healthy subjects performed yoga, golf, swimming, dance, and badminton movement activities while wearing IMUs and 10-90% of each IMU's data were randomly removed to determine the effects of data drop on estimation accuracy with and without the proposed model. Results demonstrated a reduction in RMSE of 45.2% to 51.5% in the upper limb kinematic estimation of the proposed model compared to the No Prediction strategy, and a reduction of 19.1% to 31.3% of the proposed model compared with an baseline LSTM model. In addition, the proposed model has significantly less error (p<0.05) than the No Prediction strategy and the baseline LSTM model for 10%, 20%, 30%, 40%, 50%, 60%, 70%, and 80% data drop. These results could enable wearable, wireless IMU dynamic motion analysis and assessment with reduced kinematic estimation error in the presence of varying amounts of wireless data drop and thus could further facilitate human-machine interaction and home-based medical assessment and treatment.

Older Adult Fall Risk Prediction with Deep Learning and Timed Up and Go (TUG) Test Data.

Falls are a major health hazard for older adults; therefore, in the context of an aging population, predicting the risk of a patient suffering falls in the near future is of great impact for health care systems. Currently, the standard prospective fall risk assessment instrument relies on a set of clinical and functional mobility assessment tools, one of them being the Timed Up and Go (TUG) test. Recently, wearable inertial measurement units (IMUs) have been proposed to capture motion data that would allow for the building of estimates of fall risk. The hypothesis of this study is that the data gathered from IMU readings while the patient is performing the TUG test can be used to build a predictive model that would provide an estimate of the probability of suffering a fall in the near future, i.e., assessing prospective fall risk. This study applies deep learning convolutional neural networks (CNN) and recurrent neural networks (RNN) to build such predictive models based on features extracted from IMU data acquired during TUG test realizations. Data were obtained from a cohort of 106 older adults wearing wireless IMU sensors with sampling frequencies of 100 Hz while performing the TUG test. The dependent variable is a binary variable that is true if the patient suffered a fall in the six-month follow-up period. This variable was used as the output variable for the supervised training and validations of the deep learning architectures and competing machine learning approaches. A hold-out validation process using 75 subjects for training and 31 subjects for testing was repeated one hundred times to obtain robust estimations of model performances At each repetition, 5-fold cross-validation was carried out to select the best model over the training subset. Best results were achieved by a bidirectional long short-term memory (BLSTM), obtaining an accuracy of 0.83 and AUC of 0.73 with good sensitivity and specificity values.

A novel virtual robotic platform for controlling six degrees of freedom assistive devices with body-machine interfaces

Body-machine interfaces (BoMIs)—systems that control assistive devices (e.g., a robotic manipulator) with a person’s movements—offer a robust and non-invasive alternative to brain-machine interfaces for individuals with neurological injuries. However, commercially-available assistive devices offer more degrees of freedom (DOFs) than can be efficiently controlled with a user’s residual motor function. Therefore, BoMIs often rely on nonintuitive mappings between body and device movements. Learning these mappings requires considerable practice time in a lab/clinic, which can be challenging. Virtual environments can potentially address this challenge, but there are limited options for high-DOF assistive devices, and it is unclear if learning with a virtual device is similar to learning with its physical counterpart. We developed a novel virtual robotic platform that replicated a commercially-available 6-DOF robotic manipulator. Participants controlled the physical and virtual robots using four wireless inertial measurement units (IMUs) fixed to the upper torso. Forty-three neurologically unimpaired adults practiced a target-matching task using either the physical (sample size n = 25) or virtual device (sample size n = 18) involving pre-, mid-, and post-tests separated by four training blocks. We found that both groups made similar improvements from pre-test in movement time at mid-test (Δvirtual: 9.9 ± 9.5 s; Δphysical: 11.1 ± 9.9 s) and post-test (Δvirtual: 11.1 ± 9.1 s; Δphysical: 11.8 ± 10.5 s) and in path length at mid-test (Δvirtual: 6.1 ± 6.3 m/m; Δphysical: 3.3 ± 3.5 m/m) and post-test (Δvirtual: 6.6 ± 6.2 m/m; Δphysical: 3.5 ± 4.0 m/m). Our results indicate the feasibility of using virtual environments for learning to control assistive devices. Future work should determine how these findings generalize to clinical populations.

A portable system to measure knee extensor spasticity after spinal cord injury

BackgroundThe pendulum test is a quantitative method used to assess knee extensor spasticity in humans with spinal cord injury (SCI). Yet, the clinical implementation of this method remains limited. The goal of our study was to develop an objective and portable system to assess knee extensor spasticity during the pendulum test using inertial measurement units (IMU).MethodsSpasticity was quantified by measuring the first swing angle (FSA) using a 3-dimensional optical tracking system (with external markers over the iliotibial band, lateral knee epicondyle, and lateral malleolus) and two wireless IMUs (positioned over the iliotibial band and mid-part of the lower leg) as well as a clinical exam (Modified Ashworth Scale, MAS).ResultsMeasurements were taken on separate days to assess test–retest reliability and device agreement in humans with and without SCI. We found no differences between FSA values obtained with the optical tracking system and the IMU-based system in control subjects and individuals with SCI. FSA values from the IMU-based system showed excellent agreement with the optical tracking system in individuals with SCI (ICC > 0.98) and good agreement in controls (ICC > 0.82), excellent test–retest reliability across days in SCI (ICC = 0.93) and good in controls (ICC = 0.87). Notably, FSA values measured by both systems showed a strong association with MAS scores (ρ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\uprho$$\\end{document} ~ −0.8) being decreased in individuals with SCI with higher MAS scores, reflecting the presence of spasticity.ConclusionsThese findings suggest that our new portable IMU-based system provides a robust and flexible alternative to a camera-based optical tracking system to quantify knee extensor spasticity following SCI.

Age and beta amyloid deposition impact gait speed, stride length, and gait smoothness while transitioning from an even to an uneven walking surface in older adults

BackgroundCapturing a measure of movement quality during a complex walking task may indicate the earliest signs of detrimental changes to the brain due to beta amyloid (Aβ) deposition and be a potential differentiator of older adults at elevated and low risk of developing Alzheimer's disease. This study aimed to determine: 1) age-related differences in gait speed, stride length, and gait smoothness while transitioning from an even to an uneven walking surface, by comparing young adults (YA) and older adults (OA), and 2) if gait speed, stride length, and gait smoothness in OA while transitioning from an even to an uneven walking surface is influenced by the amount of Aβ deposition present in an OA's brain. MethodsParticipants included 56 OA (>70 years of age) and 29 YA (25–35 years of age). In OA, Aβ deposition in the brain was quantified by PET imaging. All participants completed a series of cognitive assessments, a functional mobility assessment, and self-report questionnaires. Then participants performed two sets of walking trials on a custom-built walkway containing a mixture of even and uneven surface sections, including three trials with a grass uneven surface and three trials with a rocks uneven surface. Gait data were recorded using a wireless inertial measurement unit system. Stride length, gait speed, and gait smoothness (i.e., log dimensionless lumbar jerk) in the anteroposterior (AP), mediolateral (ML), and vertical (VT) directions were calculated for each stride. Outcomes were retained for five stride locations immediately surrounding the surface transition. ResultsOA exhibited slower gait (Grass: p < 0.001; Rocks: p = 0.006), shorter strides (Grass: p < 0.001; Rocks: p = 0.008), and smoother gait (Grass AP: p < 0.001; Rocks AP: p = 0.002; Rocks ML: p = 0.02) than YA, but they also exhibited greater reductions in gait speed and stride length than YA while transitioning to the uneven grass and rocks surfaces. Within the OA group, those with greater Aβ deposition exhibited decreases in smoothness with age (Grass AP: p = 0.02; Rocks AP: p = 0.03; Grass ML: p = 0.04; Rocks ML: p = 0.03), while those with lower Aβ deposition exhibited increasing smoothness with age (Grass AP: p = 0.01; Rocks AP: p = 0.02; Grass ML: p = 0.08; Rocks ML: p = 0.07). Better functional mobility was associated with less smooth gait (Grass ML: p = 0.02; Rocks ML: p = 0.05) and with less variable gait smoothness (Grass and Rocks AP: both p = 0.04) in the OA group. ConclusionThese results suggest that, relative to YA, OA may be adopting more cautious, compensatory gait strategies to maintain smoothness when approaching surface transitions. However, OA with greater Aβ deposition may have limited ability to adopt compensatory gait strategies to increase the smoothness of their walking as they get older because of neuropathological changes altering the sensory integration process and causing worse dynamic balance (i.e., jerkier gait). Functional mobility, in addition to age and Aβ deposition, may be an important factor of whether or not an OA chooses to employ compensatory strategies to prioritize smoothness while walking and what type of compensatory strategy an OA chooses.

Automated Classification of Manual Exploratory Behaviors Using Sensorized Objects and Machine Learning: A Preliminary Proof-of-Concept Study

Manual exploratory behaviors during object interaction that form the basis of tool use behavior, are mostly qualitatively characterized in terms of their frequency and duration of occurrence. To fully understand their functional and clinical significance, quantitative movement characterization is needed alongside their qualitative analysis. However, there are two challenges in quantifying them—(a) reliably classifying the type of movement and (b) performing this classification on a time series automatically. Here, we propose a machine learning-based classification method to address these challenges. We measured three common exploratory behaviors (object rotation, fingering, and throwing) in college-aged adults using “sensorized objects” that had wireless Inertial Measurement Units embedded in them. We then calculated several statistical features based on linear acceleration and angular velocity data to train machine learning classifiers to identify these behaviors. All classifiers identified the behaviors with a substantially higher accuracy (average accuracy = 84.95 ± 4.16%) than chance level (33.33%). Of all models tested, Support Vector Machine Quadratic, Support Vector Machine Medium Gaussian, and Narrow Neural Network were the best models in classifying the three behaviors (average accuracy = 89.34 ± 0.12%). This classification method shows potential for automating movement characterization of exploratory behaviors, thereby may aid early assessment of neurodevelopmental disorders.

UI-MoCap: An Integrated UWB-IMU Circuit Enables 3D Positioning and Enhances IMU Data Transmission.

While inertial measurement unit (IMU)-based motion capture (MoCap) systems have been gaining popularity for human movement analysis, they still suffer from long-term positioning errors due to accumulated drift and inefficient data transmission via Wi-Fi or Bluetooth. To address this problem, this study introduces an integrated ultrawideband (UWB)-IMU system, named UI-MoCap, designed for simultaneous 3D positioning as well as wireless IMU data transmission through UWB pulses. The UI-MoCap comprises mobile UWB tags and hardware-synchronized UWB base stations. Each UWB tag, a compact circular PCB with a 3.4cm diameter, houses a nine-axis IMU unit and a UWB transceiver for data transmission. The base stations are equipped with a UWB transceiver and an Ethernet controller, ensuring efficient reception and management of messages from multiple tags. Experiments were conducted to evaluate the system's validity and reliability of 3D positioning and IMU data transmission. The results demonstrate that UI-MoCap achieves centimeter-level 3D positioning accuracy and maintains consistent positioning performance over time. Moreover, UI-MoCap exhibits high update rates and a minimal packet loss rate for IMU data transmission, significantly outperforming Wi-Fi-based transmission techniques. Future work will explore the fusion of UWB and IMU technologies to further enhance positioning performance, with a focus on human movement analysis and rehabilitation applications.

Characterizing Bodyweight-Supported Treadmill Walking on Land and Underwater Using Foot-Worn Inertial Measurement Units and Machine Learning for Gait Event Detection.

Gait rehabilitation commonly relies on bodyweight unloading mechanisms, such as overhead mechanical support and underwater buoyancy. Lightweight and wireless inertial measurement unit (IMU) sensors provide a cost-effective tool for quantifying body segment motions without the need for video recordings or ground reaction force measures. Identifying the instant when the foot contacts and leaves the ground from IMU data can be challenging, often requiring scrupulous parameter selection and researcher supervision. We aimed to assess the use of machine learning methods for gait event detection based on features from foot segment rotational velocity using foot-worn IMU sensors during bodyweight-supported treadmill walking on land and underwater. Twelve healthy subjects completed on-land treadmill walking with overhead mechanical bodyweight support, and three subjects completed underwater treadmill walking. We placed IMU sensors on the foot and recorded motion capture and ground reaction force data on land and recorded IMU sensor data from wireless foot pressure insoles underwater. To detect gait events based on IMU data features, we used random forest machine learning classification. We achieved high gait event detection accuracy (95-96%) during on-land bodyweight-supported treadmill walking across a range of gait speeds and bodyweight support levels. Due to biomechanical changes during underwater treadmill walking compared to on land, accurate underwater gait event detection required specific underwater training data. Using single-axis IMU data and machine learning classification, we were able to effectively identify gait events during bodyweight-supported treadmill walking on land and underwater. Robust and automated gait event detection methods can enable advances in gait rehabilitation.

Estimating marksmanship performance during walking while maintaining weapon aim

Marksmanship performance while moving is a critical skill among tactical athletes due to the high demands of their occupational duties. Qualifications for dynamic marksmanship performance are not standardized across tactical athlete groups, which may limit comprehensive assessment of tactical athlete performance for situational awareness and adaptability to an unpredictable environment. Although static marksmanship performance provides foundational information on skills and level of ability, research is lacking on factors that influence dynamic marksmanship performance to best prepare tactical athletes for duties. The purpose of this study was to identify whether static marksmanship performance, speed of movement, load carriage, and biomechanical factors while ‘shooting on the move’ influenced dynamic marksmanship performance. Twenty-four male tactical athletes (22 active-duty Army Soldiers, two civilian SWAT operators; age: 23.83 ± 5.47 years; height: 1.80 ± 0.08 m; weight: 81.04 ± 7.87 kg) participated; final analyses did not include data from the two civilian operators to maintain sample homogeneity. Tactical athletes completed static and dynamic (‘shoot on the move’) marksmanship tasks under three load conditions: (1) no load (NL), (2) half kit (HK) of 11.34 kg, and (3) full kit (KIT) of 22.68 kg. Dynamic marksmanship was completed under three speed conditions: (1) self-selected slow speed, (2) standard speed, and (3) self-selected fast speed. Hip, knee, and ankle kinematics were collected via wireless inertial measurement units. Spatiotemporal parameters were collected via optical detection system. Marksmanship performance (accuracy) was collected via open-air acoustic target scoring and mean radial error (MRE) was calculated for both static and dynamic marksmanship tasks. Linear mixed-effects models were fit with dynamic MRE as the outcome variable with fixed effects of static MRE, load condition, speed condition, kinematics, and spatiotemporal parameters, adjusting for body mass. Alpha level was set a priori at p ≤ 0.10. The final statistical model included fixed effects of static MRE, load condition, speed condition, and time spent in double limb support. Static MRE (p < 0.01) and time spent in double limb support (p = 0.01) were significant factors. For each 1 cm increase in static MRE there was a 0.66 cm increase in dynamic MRE. For every 1% increase in time spent in double limb support while ‘shooting on the move’ there was a 0.13 cm increase in dynamic MRE. Findings from this study highlight that tactical athletes who have larger static stance MRE and spend a longer time in double limb support during a gait cycle exhibit an increase in MRE during ‘shoot on the move’ trials. Overall, dynamic shooting accuracy is not affected by lower extremity joint angles, load carriage, or speed of movement. Although strong relationships are known between gait speed, load, and lower extremity kinematics, the differences in tactical gait compared to normal gait and multi-task paradigm that likely favors marksmanship accuracy seem to present novel movement characteristics unique to occupational gait. Further investigation is warranted to identify other potential factors that may improve or worsen dynamic marksmanship performance.

Predicting physical activity levels from kinematic gait data using machine learning techniques

Objective analysis of gait abilities (Gait Analysis, GAn) in clinic is an essential motor assessment to improve clinical decision-making and provide precision rehabilitation approaches to recover gait functions. GAn is usually based on wearable motion sensors or camera-based systems, which generate an extensive set of data which are challenging to manage, analyse, and interpret. This makes GAn a time-consuming unfeasible assessment approach in clinical practice. Machine Learning (ML) techniques can provide a viable solution, as they can handle massive time series and complex data.This study aims to correctly classify subjects’ physical activity levels, using as ground truth a self-reported questionnaire (International Physical Activity Questionnaire, IPAQ), via kinematic features provided by wearable wireless Inertial Measurement Unit (IMU) sensors. Kinematic gait data were collected from 37 healthy subjects (24 male and 13 female) while walking on a sensorised treadmill at natural speed. Velocity, acceleration, jerk, and smoothness were calculated using the kinematic features and used to perform statistical feature extraction. The Neighbourhood Component Analysis (NCA) algorithm was used to process the statistical features space and select the most significant ones.Several models have been trained and tested before and after the feature selection to validate the approach’s effectiveness. Feature reduction resulted in a significant increase in accuracy for K-Nearest Neighbours (KNN) (81.978 ± 0.368), Random Forest (84.044 ± 3.409) and Rough-Set-Exploration-System Library K-Nearest Neighbours (RSesLib KNN) (83.956 ± 0), with an improvement of ≈20%. The performance of the best-performing classifiers was then analysed, observing the behaviour of accuracy by varying the number of features considered.

Role of the interaction between lumbar kinematics and accelerometer-measured physical activity in bodily pain, physical functioning and work ability among health care workers with low back pain.

The aim of this study was to examine the associations of spinal kinematics and physical activity (PA) with bodily pain, physical functioning, and work ability among health care workers with low back pain (LBP). Spinal kinematics and PA were measured with a wireless Inertial Measurement Unit system (ValedoMotion®) and a waist-worn tri-axial accelerometer (Hookie AM20), respectively. Their association was assessed in relation to Work Ability Index (WAI), bodily pain and physical functioning (RAND-36) in 210 health care workers with recurrent LBP. Greater lumbar movement variability/less deterministic lumbar movement (in angular velocity) during a "Pick Up a Box" functional task was correlated with higher amounts of step counts (r=-0.29, p=0.01) and moderate PA (r=-0.24, p=0.03). A higher amount of PA (p=0.03) as well as less movement control impairment (p=0.04) and movement variability (p=0.03) were associated with greater work ability, whilst greater vigorous PA was the only parameter to explain higher physical functioning (p=0.02). PA and movement variability were relative to each other to explain bodily pain (p=0.01). These findings show the importance of considering the interaction between lumbar kinematics and physical activity while planning strategies to improve bodily pain, physical functioning and work ability among health care workers with LBP.

Improving puncture accuracy in percutaneous CT-guided needle insertion with wireless inertial measurement unit: a phantom study.

A novel method applying inertial measurement units (IMUs) was developed to assist CT-guided puncture, which enables real-time displays of planned and actual needle trajectories. The method was compared with freehand and laser protractor-assisted methods. The phantom study was performed by three operators with 8, 2, and 0years of experience in CT-guided procedure conducted five consecutive needle placements for three target groups using three methods (freehand, laser protractor-assisted, or IMU-assisted method). The endpoints included mediolateral angle error and caudocranial angle error of the first pass, the procedure time, the total number of needle passes, and the radiation dose. There was a significant difference in the number of needle passes (IMU 1.2 ± 0.42, laser protractor 2.9 ± 1.6, freehand 3.6 ± 2.0 time, p < 0.001), the procedure time (IMU 3.0 ± 1.2, laser protractor 6.4 ± 2.9, freehand 6.2 ± 3.1min, p < 0.001), the mediolateral angle error of the first pass (IMU 1.4 ± 1.2, laser protractor 1.6 ± 1.3, freehand 3.7 ± 2.5 degree, p < 0.001), the caudocranial angle error of the first pass (IMU 1.2 ± 1.2, laser protractor 5.3 ± 4.7, freehand 3.9 ± 3.1 degree, p < 0.001), and the radiation dose (IMU 250.5 ± 74.1, laser protractor 484.6 ± 260.2, freehand 561.4 ± 339.8mGy-cm, p < 0.001) among three CT-guided needle insertion methods. The wireless IMU improves the angle accuracy and speed of CT-guided needle punctures as compared with laser protractor guidance and freehand techniques. • The IMU-assisted method showed a significant decrease in the number of needle passes (IMU 1.2 ± 0.42, laser protractor 2.9 ± 1.6, freehand 3.6 ± 2.0 time, p < 0.001). • The IMU-assisted method showed a significant decrease in the procedure time (IMU 3.0 ± 1.2, laser protractor 6.4 ± 2.9, freehand 6.2 ± 3.1min, p < 0.001). • The IMU-assisted method showed a significant decrease in the mediolateral angle error of the first pass and the caudocranial angle error of the first pass.

Synchronisation of wearable inertial measurement units based on magnetometer data.

Synchronisation of wireless inertial measurement units in human movement analysis is often achieved using event-based synchronisation techniques. However, these techniques lack precise event generation and accuracy. An inaccurate synchronisation could lead to large errors in motion estimation and reconstruction and therefore wrong analysis outputs. We propose a novel event-based synchronisation technique based on a magnetic field, which allows sub-sample accuracy. A setup featuring Shimmer3 inertial measurement units is designed to test the approach. The proposed technique shows to be able to synchronise with a maximum offset of below 2.6ms with sensors measuring at 100Hz. The investigated parameters suggest a required synchronisation time of 8 s. The results indicate a reliable event generation and detection for synchronisation of wireless inertial measurement units. Further research should investigate the temperature changes that the sensors are exposed to during human motion analysis and their influence on the internal time measurement of the sensors. In addition, the approach should be tested using inertial measurement units from different manufacturers to investigate an identified constant offset in the accuracy measurements.

Feasibility of Bluetooth Low Energy for motion capturing with Inertial Measurement Units

Wireless Inertial Measurement Units provide motion capture data with a low hardware cost while offering a lot of mobility for the user. The current solutions rely on Wi-Fi or custom radio protocols, which are usually access point-centre having all the traffic routed through a single access point, limiting direct interaction capabilities with the surrounding devices. Bluetooth Low Energy (BLE) forms point-to-point networks directly between two devices without additional networking, thus, a BLE based motion capture suit could enable seamless direct cooperation between two robots or a human and a robot.In this paper, the feasibility of BLE 5 for motion capturing is investigated by designing and testing an implementation of such a motion capture system using existing commercial hardware. More specifically, a mobile phone was utilized as the receiver device for real-time visualization, whereas BLE sensors were used in order to test the feasibility of the technology for actual use and to identify the bottlenecks in the system for future optimizations. The designed prototype system achieved similar or better performance for the static accuracy and delays in the system when compared to the currently existing commercial suits, while providing a feasible throughput for real-time motion capturing.

Cognitive-motor interference in people with mild to moderate multiple sclerosis, in comparison with healthy controls

BackgroundReduced motor and cognitive dual-task capacity is found to be more common among people with multiple sclerosis (MS), than among healthy populations. However, studies in larger samples of MS conducted using a more stringent methodology, which includes comparisons to healthy controls, are needed. Thus, the primary aim of this study was to explore the effects on motor and cognitive dual-tasking in people with mild to moderate overall MS-disability, in comparison to healthy controls. A second aim was to explore the differences in dual-task performance on a cognitive task between two motor tasks in people with mild to moderate MS and healthy controls. MethodsThis case-control study evaluated dual-task performance of the motor tasks standing with eyes closed (hereafter standing) and walking and a cognitive task assessing selective executive functions (auditory-Stroop test). Fifty-five people with MS (mild MS, n = 28; moderate MS, n = 27), and 30 healthy controls participated. Standing and walking were assessed using wireless inertial measurement unit sensors (APDM). Standing (three 30 s trials) was measured using sway area and root mean square sway, while walking (2 min) was measured using speed, stride length, and step time. Auditory-Stroop was measured using accuracy and response time. During dual-task assessments, each subject was instructed to pay equal attention to both tasks. Statistical significance was considered if p < .05. ResultsInstanding no significant within-group differences in the standing measures were found between single-task and dual-task performance. However, dual-task performance differed significantly between all groups (moderate MS > mild MS > healthy controls), except between mild and moderate MS in sway area. Inwalking, all groups slowed down speed and shortened stride length during dual-task condition compared to single-task condition. Moderate MS performed significantly poorer than mild MS and healthy controls in dual-task walking, but mild MS did not differ from healthy controls. In thecognitivetask only mild MS increased significantly in auditory-Stroop response time during walking. In healthy controls, the performance of auditory-Stroop was not affected by dual-tasking. Moderate MS had significantly longer response time in dual-task auditory-Stroop compared to the other groups, but no differences were observed between mild MS and healthy controls. Only mild MS had significantly longer response time during walking than during standing. ConclusionThis study showed that cognitive-motor interference in people with MS is present also in the early phases of the disease. This was shown during dual-tasking with slower walking and a longer response time in the cognitive task compared to healthy controls. Moderate MS performed poorer in almost every aspect of the motor and cognitive assessments in dual-task condition, compared to mild MS and healthy controls. Furthermore, during standing, people with MS performed poorer in standing measures compared to healthy controls. Additionally, healthy controls showed no cognitive interference during motor tasks.The results suggest that standardized regular assessment of dual-tasking in MS care might increase the individual's knowledge of dual-task capacity and contribute to understanding of possible related consequences. However, feasible assessment equipment and specific motor-cognitive dual-task training interventions for people with MS need to be developed.

Does visual cueing improve gait initiation in people with Parkinson's disease?

Anticipatory postural adjustments (APAs) prior to gait initiation are impaired in people with Parkinson's disease (PD), particularly in those who report Freezing of Gait (FOG). External cues can improve gait parameters in people with PD, but the effects of visual cues on gait initiation are poorly known. The study aimed to (i) assess differences, during gait initiation, between people with PD with (FOG+) and without FOG (FOG-) and healthy controls (HC), (ii) explore the effect of disease severity on gait initiation and (iii) investigate the acute effect of visual cueing on gait initiation and straight-ahead gait. Twenty FOG- and twenty FOG+, and eighteen HC participated in this study. Participants were asked to perform self-initiated gait with and without visual cues presented as transverse taped lines on the floor. Gait initiation and gait were characterized with wireless inertial measurement units. Results showed that FOG+ had smaller APAs than HC and FOG-; although no differences were detected between FOG+ and FOG- when taking into account disease severity. Significant correlations between MDS-UPDRS III scores and gait initiation/straight-ahead gait variables confirmed that differences between FOG+ and FOG- were driven by disease severity. In gait initiation, visual cues elicited different behaviors in people with and without PD. Particularly, people with PD showed smaller and longer APAs, whereas HC showed longer first step durations, compared to baseline. However, the adopted visual cues improved gait speed and stride length in all individuals. These results suggest that people with PD, despite the presence of FOG, utilize different motor strategies, compared to HC, to adapt to the new biomechanical requirements of gait initiation dictated by the visual cues.

Test-retest reliability of wireless inertial-sensor derived measurements of knee joint kinematics.

Advances in sensor technology have provided an opportunity to measure gait characteristics using body-worn inertial measurement units (IMUs). Whilst research investigating the validity of IMUs in reporting gait characteristics is extensive, research investigating the reliability of IMUs is limited. This study aimed to investigate the inter-session reliability of wireless IMU derived measures of gait (i.e., knee angle, range of motion) taking multiple test administrators into account. Fifteen healthy volunteers (43 ± 15 years) completed two visits. Within each visit, participants were required to perform two sets of 6 gait trials (6-metre walk tests). IMUs were placed on the participant in 7 locations on the lower limbs and waist. A different test administrator (n = 3) applied the IMUs at each set. At visit 2, this procedure was repeated with the same test administrators as visit 1. Kinematic measures of maximum angle (Knee_Max), minimum angle (Knee_Min), and range of motion (RoM) are reported for the left and right knee. The intraclass correlation coefficients (ICC), standard error of measurement (SEM) and minimum detectable change (MDC) are reported to determine IMU reliability. The results confirmed moderate to good inter-session reliability across all features (0.73-0.87). SEM values ranged from 1.21-3.32° and MDC values ranged from 3.37 - 9.21°. Therefore, IMUs appear to be a reliable method to determine inter-session gait characteristics across multiple test administrators.

Using wireless inertial measurement units for measuring hip range of motion through commonly used clinical tests

Hamstring extensibility is a crucial component of athletic performance and spinal health. In this regard, hip range of motion analysis through standardized clinical tests has been used as a valid and reliable method for measuring hamstring extensibility. Consequently, new inertial devices have been designed for this purpose. The aims of this study were to: (i) analyze the concurrent validity of inertial sensors for measuring hamstring extensibility through the Active Straight Leg Raise (ASLR) and Passive Straight Leg Raise (PSLR) tests; and (ii) analyze the test-retest reliability of inertial sensors for measuring hamstring extensibility through the ASLR and PSLR tests. A total of 18 healthy participants took part in this cross-sectional study. The hamstring extensibility was measured through the range of motion of the left and right hip in the Active Straight Leg Raise (ASLR) and Passive Straight Leg Raise (PSLR) tests. Data were collected by inertial sensors (WIMU Pro) and an ISOMED bi-level inclinometer, which was used as the reference instrument. The inertial sensors reported a standard error of the measurement (SEM) below 0.6° in all measurements. The R2 correlation and the intraclass correlation coefficient (ICC) were very close to 1 in all measurements. Regarding the reliability analysis, there were no significant differences ( p &gt; 0.05) between test and retest measures, and the SEM were below 0.8° in both instruments. The ICC were close to 1 in all cases as well. The coefficients of variation (CV) were below 2.7% in the inertial device and 2.2% in the inclinometer. This study showed that using microtechnology through WIMU Pro may be a valid and reliable method for measuring hip range of motion during the ASLR and PSLR tests.

Four-limb wireless IMU sensor system for automatic gait detection in canines

This study aims to develop a 4-limb canine gait analysis system using wireless inertial measurement units (IMUs). 3D printed sensor holders were designed to ensure quick and consistent sensor mounting. Signal analysis algorithms were developed to automatically determine the timing of swing start and end in a stride. To evaluate the accuracy of the new system, a synchronized study was conducted in which stride parameters in four dogs were measured simultaneously using the 4-limb IMU system and a pressure-sensor based walkway gait system. The results showed that stride parameters measured in both systems were highly correlated. Bland–Altman analyses revealed a nominal mean measurement bias between the two systems in both forelimbs and hindlimbs. Overall, the disagreement between the two systems was less than 10% of the mean value in over 92% of the data points acquired from forelimbs. The same performance was observed in hindlimbs except for one parameter due to small mean values. We demonstrated that this 4-limb system could successfully visualize the overall gait types and identify rapid gait changes in dogs. This method provides an effective, low-cost tool for gait studies in veterinary applications or in translational studies using dog models of neuromuscular diseases.