Inertial Sensor System Research Articles

Sensor-based gait analysis in the premotor stage of LRRK2 G2019S-associated Parkinson's disease

IntroductionThere is a need for biomarkers to monitor the earliest phases of Parkinson's disease (PD), especially in premotor stages. Here, we studied whether there are early gait alterations in carriers of the G2019S mutation of LRRK2 that can be detected by means of an inertial sensor system. MethodsTwenty-one idiopathic PD patients, 20 LRRK2-G2019S PD, 27 asymptomatic carriers of LRRK2-G2019S mutation (AsG2019S) and 36 controls walked equipped with 16 lightweight inertial sensors in three different experiments: i/normal gait, ii/fast gait and iii/dual-task gait. In the AsG2019S group, DaT-SPECT (123I-ioflupane) with semi-quantitative analysis was carried out. Motor and cognitive performance were evaluated using MDS-UPDRS-III and MoCA scales. We employed neural network techniques to classify individuals based on their walking patterns. ResultsPD patients and controls showed differences in speed, stride length and arm swing amplitude, variability and asymmetry in all three tasks (p < 0.01). In the AsG2019S group, the only differences were detected during fast walking, with greater step time on the non-dominant side (p < 0.05), lower step/stride time variability (p < 0.01) and lower step time asymmetry (p < 0.01). DaT uptake showed a significant correlation with step time during fast walking on the non-dominant side (r = −0.52; p < 0.01). The neural network was able to differentiate between AsG2019S and healthy controls with an accuracy rate of 82.5%. ConclusionOur sensor-based analysis did not detect substantial and robust changes in the gait of LRRK2-G2019S asymptomatic mutation carriers. Nonetheless, step or stride time during fast walking, supported by the observed correlation with striatal DaT binding deserves consideration as a potential biomarker in future studies.

Wireless inertial sensor system for hammer throwing

Abstract The aim of this study is to integrate an inertial sensor inside a hammer to allow a realtime feedback. In the first step we build our own prototype to measure the radial acceleration. In the second step there is a validation with an infrared camera system. It is a comparison between the radial acceleration along the wire axis, that is measured by the sensor against the velocity that is delivered by the infrared camera system. As a result, significant correlation was observed between the measured velocity and the acceleration (r = 0.99, p &lt; 0.001). These suggest that this system can used in the training to improve the technique of the hammer throw.

Test-Retest Reliability of Kinematic and Temporal Outcome Measures for Clinical Gait and Stair Walking Tests, Based on Wearable Inertial Sensors.

It is important to assess gait function in neurological disorders. A common outcome measure from clinical walking tests is average speed, which is reliable but does not capture important kinematical and temporal aspects of gait function. An extended gait analysis must be time efficient and reliable to be included in the clinical routine. The aim of this study was to add an inertial sensor system to a gait test battery and analyze the test-retest reliability of kinematic and temporal outcome measures. Measurements and analyses were performed in the hospital environment by physiotherapists using customized software. In total, 22 healthy persons performed comfortable gait, fast gait, and stair walking, with 12 inertial sensors attached to the feet, shank, thigh, pelvis, thorax, and arms. Each person participated in 2 test sessions, with about 3–6 days between the sessions. Kinematics were calculated based on a sensor fusion algorithm. Sagittal peak angles, sagittal range of motion, and stride frequency were derived. Intraclass-correlation coefficients were determined to analyze the test-retest reliability, which was good to excellent for comfortable and fast gait, with exceptions for hip, knee, and ankle peak angles during fast gait, which showed moderate reliability, and fast gait stride frequency, which showed poor reliability. In stair walking, all outcome measures except shoulder extension showed good to excellent reliability. Inertial sensors have the potential to improve the clinical evaluation of gait function in neurological patients, but this must be verified in patient groups.

Monocular Visual-Inertial-Wheel Odometry Using Low-Grade IMU in Urban Areas

In this article, we propose a new methodology to fuse visual-inertial measurements for land vehicles in a challenging urban environment in which a GNSS signal is not available nor reliable. Motivated by a degenerate case caused by a large bias of a MEMS IMU, we redesign a system model of visual-inertial odometry in a framework of extended Kalman filter. In particular, the system model is propagated through a reduced inertial sensor system composed of a 3-axis gyroscope, a 2-axis accelerometer, and a single-axis odometer. An analytical observability derivation reveals unobservable bases of our estimator, and these directions are resolved by using intermittent position measurements from a GNSS receiver. Furthermore, we inspect the uncertainties of the state vector in a Monte-Carlo simulation that agrees with our theoretical results. The proposed method is validated through the KITTI benchmark dataset and an extensive field testing showing a position drift as 1.25% in tunnels on average and a mean position error of 2.81m in the street canyon over a 6.7km driving.

Optimal Configuration Based on Lever Arm Effect Rejection Performance Index for Redundant Inertial Sensors

Redundant inertial sensor systems improve navigation performance and guarantee fault detection or fault isolation functions. However, they may cause unexpected side effects owing to their enlarged size. The misalignment between the center of gravity of the sensor system and the rotational origin of the vehicle results in the lever arm effect inducing significant acceleration errors. To minimize the lever arm effect error, the configuration of redundant sensors should be carefully considered, as their performance is highly dependent on its configuration. Thus, the figure-of-merit (FOM), which is considered a performance index or cost function, is proposed to obtain the optimal configuration for minimizing the lever arm effect error. Further, to define an FOM the angular velocity and angular acceleration are considered as random variables and to quantify them, they were replaced as variances. The lever arm effect error was found to be completely eliminated when a redundant inertial measurement unit (RIMU) with more than eight single-axis inertial sensors was configured using the proposed FOM. Further, a case study and simulation were conducted to verify the proposed FOM and configuration, and it was found that the proposed FOM can be used to obtain the optimal configuration of the RIMU and as an indicator of the lever arm effect error.

Comparison of 2% mepivacaine and a solution of 2% lidocaine/epinephrine administered for median and ulnar nerve blocks in horses with naturally occurring forelimb lameness.

To compare a 2% lidocaine solution containing 5μg/ml (1:200 000) epinephrine with 2% mepivacaine for reducing lameness in horses after use in proximal nerve blocks. Experimental randomized crossover. Six adult horses with naturally occurring forelimb lameness. Horses were evaluated using an inertial gait sensor system. Lameness was measured as a vector sum (VS). Following baseline lameness examination, median and ulnar nerve blocks were performed with lidocaine/epinephrine (0.5mg epinephrine added to 50 ml of 2% lidocaine immediately prior to administration) or an equal volume of 2% mepivacaine. Horses were trotted at 5min and then at 30 min intervals for 150 min. After 24 h, nerve blocks were repeated using the other local anesthetic. Data were evaluated using linear models. The reduction in the VS did not differ after nerve blocks with lidocaine/epinephrine or mepivacaine (P=.791). Mean time to VS <8.5 mm (n=5) was 5 and 9.6min for lidocaine/epinephrine and mepivacaine, respectively. For one horse, VS was not reduced to <8.5 mm with either treatment (this horse had the highest VS before treatments were administered). The decrease in VS to <8.5 mm lasted for 150 min in both treatment groups. The outcomes of the median and ulnar nerve blocks performed with 2% lidocaine with epinephrine did not differ from blocks performed with 2% mepivacaine. Two percent lidocaine with epinephrine may serve as an adequate replacement for proximal nerve blocks when mepivacaine is unavailable.

Lumbar Flexion During Driving: Establishing a Methodology for Characterising Real-Time Posture Data Collected by Innovative Technology.

To determine a methodology for the analysis of real-time driving posture data in the low back pain population. The strength of the relationship between driving posture and low back pain is yet to be defined due to the lack of studies in the field using validated and repeatable posture measurement tools. Reliable and validated real-time measurement tools are now available, yet reliable methods of analysis of these data are yet to be established. Ten occupational drivers completed a typical work shift while wearing an inertial motion sensor system (dorsaVi ViMove). Real-time lumbar flexion data were extracted, with test-retest reliability of mean lumbar flexion, peak lumbar flexion, and standard deviation of lumbar flexion analysed at different times across a work shift, and in different sections within a drive. Mean lumbar flexion was highly repeatable over numerous drives in one day, with greater test-retest reliability if the first five minutes of driving data were excluded. Peak lumbar flexion had acceptable test-retest reliability over numerous drives in one day, while standard deviation of lumbar flexion was not a repeatable measure. Mean lumbar flexion was a reliable outcome for characterising driving posture in drivers with low back pain. Peak lumbar flexion may be used if appropriate to the individual study. Standard deviation of lumbar flexion is not a reliable posture outcome. This paper provides a reliable methodology for analysis of real-time driving posture data in occupational drivers with low back pain.

Differences in walking-to-turning characteristics between older adult fallers and nonfallers: a prospective and observational study using wearable inertial sensors.

Wearable inertial sensors have gradually been used as an objective technology for biomechanical assessments of both healthy and pathological movement patterns. This paper used foot-worn sensors for characterizing the spatiotemporal characteristics of walking and turning between older fallers and nonfallers. Thirty community-dwelling older fallers and 30 older nonfallers performed 10-m straight walking, turned 180° around a cone, and then walked 10-m back to the starting point. Specific algorithms were used to measure spatiotemporal gait (double support phase of the gait cycle, swing width, and minimal toe clearance) and turning parameters (turn duration and turn steps) using two foot-worn Physiolog inertial sensor system. The researchers directly exported data as reported by the system. Our findings indicated that older fallers showed 26.58% longer time (P = 0.036) and 13.21% more steps (P = 0.038) compared to nonfallers during turning. However, both groups decreased their walking velocity (both P < 0.001), increased double support (both P = 0.001), and increased the swing width (both P = 0.001) during the transition from walking to turning. The older nonfallers additionally increased toe clearance (P = 0.001). Compared with the fallers, the older nonfallers showed a larger change in the swing width (P = 0.025) and toe clearance (P = 0.025) in walking to turning. Older fallers may adopt a cautionary strategy while turning to reduce the risk of falls. Wearable sensors can provide the temporospatial characteristics of turning and reveal significant differences by fall status, indicating the potential of turning measures as possible markers for identifying those at fall risk.

Normative database of postural sway measures using inertial sensors in typically developing children and young adults.

Reference values utilizing the APDM MobilityLab® inertial sensor system have not been established in children and young adults ages 5-30. These values are necessary for clinicians and researchers to compare to children with balance impairments. A group of 144 typically developing children and young adults from age 5-30 years completed the instrumented SWAY test during 6 test conditions: normal stance, firm surface, eyes open (EO) and closed (EC); normal stance, foam surface, EO and EC; and tandem stance, firm surface, EO and EC. Selected variables for normative outcomes included total sway area, and the mean, sagittal and coronal values for RMS sway, jerk, sway velocity and path length. Sex differences were examined within age groups via t tests. The effect of age on postural sway variables was analyzed using a one-way ANOVA for the mean values of total sway area, RMS sway, velocity and jerk, followed by post-hoc pairwise comparisons. All sway parameters decreased significantly with age (p < 0.0001). Adult-like total sway area and jerk were achieved by ages 9-10 except for jerk during EC on foam. RMS sway and sway velocity reached adult levels by ages 11-13 during all EO and tandem stance conditions, and 14-21 with EC during normal stance on firm and foam surfaces for RMS sway and EC on firm surfaces for velocity. Females ages 5-6 performed more poorly during EO firm and EC foam for certain variables, but better during EO tandem and females ages 7-13 outperformed males when sex differences were found. These reference values can now be used by clinicians and researchers to evaluate abnormal postural sway and response to interventions in children and young adults.

Feature Extraction and Data Analysis of Basketball Motion Postures: Acquisition With an Inertial Sensor

Abstract This paper mainly analyzed the application of inertial sensors in basketball posture analysis. The data of 20 basketball players in different postures were collected by Micro-electromechanical systems inertial sensors. The mean, variance, and skewness were taken as features to compare the performance of C4.5, random forest (RF), k-nearest neighbor, and support vector machine (SVM) algorithms in analyzing posture data. It was found that the classification accuracy of the k-nearest neighbor algorithm was around 90%, and the classification accuracy of C4.5, RF, and SVM algorithms was all above 90%. The classification accuracy of the RF algorithm was the highest (98.72%), which was significantly higher than C4.5 and SVM algorithms. The results verified the advantages of the RF algorithm in basketball posture analysis. The research results confirm the reliability of the inertial sensor in the field of motion posture analysis and make some contributions to its application in sport training. This paper provides support for the analysis of motion posture.

Influence of an on-body lifting aid (HAL® for Care Support) on kinematics during repetitive lifting in healthy men

Work-related lower back pain (LBP) leads to socio-economic burden and demands solutions. The hybrid assistive limb (HAL) for Care Support (Cyberdyne Inc., Ibaraki, Japan) is an active on-body lifting aid to assist joint motion according to the wearer’s voluntary motor drive to reduce the lumbar load. A few studies investigated HAL and stated efficacy in terms of enhanced performance and reduced fatigue, yet the question remained if the use of HAL may result in a different execution of movement, for example by influencing the kinematics of the lower extremities. The aim of this study was to determine the influence of HAL on kinematics of the lower limbs and the spinal column during repetitive freestyle symmetrical lifting. Kinematic data was recorded by an inertial measurement unit sensor system in 11 healthy men lifting and lowering a 19.5 kg barbell under three conditions (no HAL, HAL Level 3/5, HAL Level 5/5). Outcome parameters were maximum and minimum angles as well as range of motion (ROM) of thoracic spine, lumbar spine, hip- and knee joint in sagittal plane. We found a significantly decreased ROM of the lumbar spine as well as a significantly reduced maximum and minimum thorax extension when starting lifting and in upright position after lifting, respectively, while using HAL. Influence of HAL on the kinematics of the lower limbs was not significant. Differences between both evaluated HAL conditions were not significant. This study proved limited lumbar spine ROM and reduced thorax extension without alterations of lower limbs kinematics when using HAL. This might potentially decrease the risk of work-related LBP.

Online Nonlinear Error Compensation Circuit Based on Neural Networks

Nonlinear errors of sensor output signals are common in the field of inertial measurement and can be compensated with statistical models or machine learning models. Machine learning solutions with large computational complexity are generally offline or implemented on additional hardware platforms, which are difficult to meet the high integration requirements of microelectromechanical system inertial sensors. This paper explored the feasibility of an online compensation scheme based on neural networks. In the designed solution, a simplified small-scale network is used for modeling, and the peak-to-peak value and standard deviation of the error after compensation are reduced to 17.00% and 16.95%, respectively. Additionally, a compensation circuit is designed based on the simplified modeling scheme. The results show that the circuit compensation effect is consistent with the results of the algorithm experiment. Under SMIC 180 nm complementary metal-oxide semiconductor (CMOS) technology, the circuit has a maximum operating frequency of 96 MHz and an area of 0.19 mm2. When the sampling signal frequency is 800 kHz, the power consumption is only 1.12 mW. This circuit can be used as a component of the measurement and control system on chip (SoC), which meets real-time application scenarios with low power consumption requirements.

Implementation of Parallel Cascade Identification at Various Phases for Integrated Navigation System

Global navigation satellite systems (GNSS) are widely used for the navigation of land vehicles. However, the positioning accuracy of GNSS, such as the global positioning system (GPS), deteriorates in urban areas due to signal blockage and multipath effects. GNSS can be integrated with a micro-electro-mechanical system (MEMS)–based inertial navigation system (INS), such as a reduced inertial sensor system (RISS) using a Kalman filter (KF) to enhance the performance of the integrated navigation solution in GNSS challenging environments. The linearized KF cannot model the low-cost and small-size sensors due to relatively high noise levels and compound error characteristics. This paper reviews two approaches to employing parallel cascade identification (PCI), a non-linear system identification technique, augmented with KF to enhance the navigational solution. First, PCI models azimuth errors for a loosely coupled 2D RISS integrated system with GNSS to obtain a navigation solution. The experimental results demonstrated that PCI improved the integrated 2D RISS/GNSS performance by modeling linear, non-linear, and other residual azimuth errors. For the second scenario, PCI is utilized for modeling residual pseudorange correlated errors of a KF-based tightly coupled RISS/GNSS navigation solution. Experimental results have shown that PCI enhances the performance of the tightly coupled KF by modeling the non-linear pseudorange errors to provide an enhanced and more reliable solution. For the first algorithm, the results demonstrated that PCI can enhance the performance by 77% as compared to the KF solution during the GNSS outages. For the second algorithm, the performance improvement for the proposed PCI technique during the availability of three satellites was 39% compared to the KF solution.

Biomechanical Changes in Gait Patterns of Patients with Grade II Medial Gonarthritis.

Deforming osteoarthritis (OA) of the knee is a progressive disease associated with pain and compromised function of the joint. Typical biomechanical modifications in the gait of subjects with medial knee OA are characterized by decreased gait speed and overload on the affected limb. The borderline stage for conservative versus surgical management is Grade II OA. The aim of this research was to study preoperatively the specific features of gait, knee, and hip function in patients with Grade II medial OA. We examined 26 patients with Grade II unilateral gonarthritis with varus deformity and 20 healthy adults. Biomechanical parameters of gait were recorded using an inertial sensor system. The gait cycle (GC) slightly increased both for the affected and for the intact limb. The hip joint movements showed significant symmetrical reduction in the first flexion amplitude, as well as a symmetrical delay in full hip extension at the end of the stance phase. In the knee, the first flexion amplitude was significantly reduced on the affected side compared to healthy control. The extension amplitude in the single support phase was significantly increased in both the affected and the intact lower limbs. The swing amplitude was significantly reduced on the affected side. On the affected side, the changes were more pronounced, both in incidence and in severity. The affected knee showed a syndrome of three reduced amplitudes. In patients, walking is characterized by several groups of symptoms: those of unloading of the affected limb, those of limiting the load on the affected joint and the musculoskeletal system as a whole, and those of gait harmonization. The symptoms of unloading the affected side and those of harmonization are the common symptoms of adaptation, typical for several pathological conditions with a relatively preserved function. The intensity of the observed symptoms can help assess changes in the subject’s functional condition over time and during the treatment.

Do hip-abduction braces work?—A biomechanical evaluation of a commercially available hip brace

IntroductionDislocations of the hip joint are a common and clinically relevant complication following total hip arthroplasty (THA). Hip-abduction braces are currently used following operative or non-operative treatment of THA dislocations to prevent re-dislocations. However, the clinical and biomechanical effectiveness of such braces is still controversial.Material and methodsA total of 30 volunteers were measured during standing and during sitting up and down from a chair task wearing a hip brace set at 70°, 90° or no hip flexion limitation. Range of motion of the hip joint was measured in all directions by an inertial sensor system. Further it has been evaluated if the range of motion would be reduced by the additional use of an arthrodesis cushion.ResultsThe use of a hip brace set up with flexion limitation did reduce hip ROM in all directions significantly compared to unhinged brace (p < 0.001–0.035). Performing the “sit down and stand-up task” the brace set up at 70° flexion limitation did reduce maximum hip flexion significantly (p = 0.008). However, in most cases the measured hip flexion angles were greater than the settings of the hip brace should have allowed. The additional use of a cushion can further limit hip motion while sitting up and down from a chair.ConclusionThis study has demonstrated that hip-abduction braces reduce hip range of motion. However, we also found that to achieve a flexion limitation of the hip to 90°, the hip brace should be set at a 70° hip flexion limitation.

Development and validation of smartwatch-based activity recognition models for rigging crew workers on cable logging operations.

Analysis of high-resolution inertial sensor and global navigation satellite system (GNSS) data collected by mobile and wearable devices is a relatively new methodology in forestry and safety research that provides opportunities for modeling work activities in greater detail than traditional time study analysis. The objective of this study was to evaluate whether smartwatch-based activity recognition models could quantify the activities of rigging crew workers setting and disconnecting log chokers on cable logging operations. Four productive cycle elements (travel to log, set choker, travel away, clear) were timed for choker setters and four productive cycle elements (travel to log, unhook, travel away, clear) were timed for chasers working at five logging sites in North Idaho. Each worker wore a smartwatch that recorded accelerometer data at 25 Hz. Random forest machine learning was used to develop predictive models that classified the different cycle elements based on features extracted from the smartwatch acceleration data using 15 sliding window sizes (1 to 15 s) and five window overlap levels (0%, 25%, 50%, 75%, and 90%). Models were compared using multiclass area under the Receiver Operating Characteristic (ROC) curve, or AUC. The best choker setter model was created using a 3-s window with 90% overlap and had sensitivity values ranging from 76.95% to 83.59% and precision values ranging from 41.42% to 97.08%. The best chaser model was created using a 1-s window with 90% overlap and had sensitivity values ranging from 71.95% to 82.75% and precision values ranging from 14.74% to 99.16%. These results have demonstrated the feasibility of quantifying forestry work activities using smartwatch-based activity recognition models, a basic step needed to develop real-time safety notifications associated with high-risk job functions and to advance subsequent, comparative analysis of health and safety metrics across stand, site, and work conditions.

Attitude determination in integrated GNSS-inertial navigation systems in information and measurement insufficiency conditions

The article presents the attitude determination algorithm for multi-antenna global navigation satellite and inertial integrated navigation system operating under information and measurement insufficiency conditions. The information and measurement insufficiency conditions are understood as the impossibility of the inertial system alignment on a fixed base and the absence of a redundant number of satellites to resolve the carrier phase integer ambiguity. The ability of the navigation system to operate in such conditions is achieved by combining several existing attitude determination methods and using the new method of attitude determination using ambiguous satellite phase measurements and absolute angular rate measurements. The hardware-in-the-loop simulation results using a 2-axis motion simulator and microelectromechanical systems inertial sensors are presented. The results confirm the efficiency of the proposed approach to the design of the navigation system algorithm.

고령자와 젊은 성인의 선호속도보행 시 보행리듬 제한에 따른 보행 패턴의 변화: 기초연구

Purpose: The purpose of this study is to compare the age-specific changes according to the gait rhythm restriction using a metronome when walking at a preferred speed for older and young adults.<BR> Method: Twenty-nine healthy young adults (age: 22.8±2 years old, 15 males & 14 females) and 24 elderly people (age: 76.5±1 years old, 11 males & 13 females, BBS score: 54.3±1) participated in the experiment. The gait experiment was configured to walk twice pre subject at a preferred speed for 6 minutes in a 50m long corridor. The gait consisted of two conditions: walking at the preferred speed (PWS) and walking at the preferred speed with metronome rhythm(PWS+met). For the analysis, 19 variables composed of 4 categories (7 spatiotemporal variables, 3 lower limb movement variables, 6 upper body movement variables, and 3 turning variables) were used. An inertial sensor system was used to measure the gait variables. Repeated measures ANOVA were performed to compare gait variables by age according to gait conditions. The significant level of hypothesis verification is set up as α =.05.<BR> Results: There was a difference in all gait categories compared between healthy older and young adults when walking at the preferred speed (p<.05). However, in the within-group comparison according to the gait rhythm restriction, there were significantly statistical differences in the time variable and the turning variable categories during metronomic walking in the case of young adults p(<.05), while there were significant differences in the spatial variable and lower limb movement categories in the case of the elderly (p<.05).<BR> Conclusion: In summary, this study provides basic data that can confirm gait adaptation patterns by age when walking using a simple cognitive-motor dual task such as a metronome.

An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities.

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.

Inertial measurement units for the detection of the effects of simulated leg length inequalities

BackgroundLeg length inequalities (LLI) are a common condition that can be associated with detrimental effects like low back pain and osteoarthritis. Inertial measurement units (IMUs) offer the chance to analyze daily activities outside a laboratory. Analyzing the kinematic effects of (simulated) LLI on the musculoskeletal apparatus using IMUs will show their potentiality to improve the comprehension of LLI.MethodsTwenty healthy participants with simulated LLI of 0-4 cm were analyzed while walking with an inertial sensor system (MyoMotion). Statistical evaluation of the peak anatomical angles of the spine and legs were performed using repeated measurement (RM) ANOVA or their non-parametric test versions (Friedman test).ResultsLumbar lateral flexion and pelvic obliquity increased during the stance phase of the elongated leg and decreased during its swing phase. The longer limb was functionally shortened by higher hip and knee flexion, higher hip adduction, dorsiflexion, and lower ankle adduction. Finally, the shorter leg was lengthened by higher hip and knee extension, hip abduction, ankle plantarflexion, and decreased hip adduction.ConclusionWe found differing compensation strategies between the different joints, movement planes, gait phases, and amounts of inequality. Overall the shorter leg is lengthened and the longer leg is shortened during walking, to retain the upright posture of the trunk. IMUs were helpful and precise in the detection of anatomical joint angles and for the analysis of the effects of LLI.