INERTIAL SENSOR-BASED MOTION ANALYSIS SYSTEM OF BRIDGE-STYLE MOVEMENT FOR REHABILITATION TREATMENTS

2021 ◽  
Author(s):  
GUANGSHUAI ZHANG ◽  
CHUNBAO WANG ◽  
JIANJUN LONG ◽  
QUANQUAN LIU ◽  
JIANJUN WEI ◽  
...  
Keyword(s):  
Lower Limb ◽  
Evaluation Method ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Movement Planning ◽  
Necessary Condition ◽  
Position Change ◽  
Coordination Ability ◽  
Analysis System ◽  
Bed Position

In the clinical course of the treatment, impartial representation of the patients’ rehabilitation state is a necessary condition for taking the best treatment to match the state of the current recovery. Bridge-style movement is one of the earliest training programs of the bed position change and is also the basis of successful standing and walking training because the bridge-style movement can inhibit the spasticity pattern of lower limb extensors and improve the control and coordination ability from the pelvis to lower limb. However, patients’ bridge-style movement planning for the current rehabilitation state largely depends on therapists’ clinical experience and subjective that may deteriorate the rehabilitation effect. Thus, it is necessary for hemiplegic patients to develop quantitative motor function assessment to judge its current rehabilitation state. This paper proposes a quantitative evaluating method to detect patients’ bridge-style movement posture and analyze their motion abilities. The real-time postural change of the bridge-style movement can be acquired by the inertial sensors attached to the waist, thigh, and crus. The bridge-style movement process of patients is recorded and analyzed by the software processing program. Finally, the experiment can be carried out to verify the feasibility and correctness of the evaluation method. The experimental results show that the evaluation method can judge patients’ current motion ability and rehabilitation state. And it is helpful for therapists to carry out targeted training for patients’ state.

scholarly journals Experimental Validation of the Tyndall Portable Lower-limb Analysis System with Wearable Inertial Sensors

2016 ◽  
Vol 147 ◽  
pp. 208-213 ◽  
Author(s):  
Salvatore Tedesco ◽  
Andrea Urru ◽  
Amanda Clifford ◽  
Brendan O’Flynn
Keyword(s):  
Lower Limb ◽  
Experimental Validation ◽  
Inertial Sensors ◽  
Analysis System ◽  
Wearable Inertial Sensors

scholarly journals Automatic pairing of inertial sensors to lower limb segments – a plug-and-play approach

2016 ◽  
Vol 2 (1) ◽  
pp. 715-718 ◽  
Author(s):  
David Graurock ◽  
Thomas Schauer ◽  
Thomas Seel
Keyword(s):  
Sensor Networks ◽  
Lower Limb ◽  
Healthy Subjects ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Lower Limbs ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Correct Pairing ◽  
Measurement Units

AbstractInertial sensor networks enable realtime gait analysis for a multitude of applications. The usability of inertial measurement units (IMUs), however, is limited by several restrictions, e.g. a fixed and known sensor placement. To enhance the usability of inertial sensor networks in every-day live, we propose a method that automatically determines which sensor is attached to which segment of the lower limbs. The presented method exhibits a low computational workload, and it uses only the raw IMU data of 3 s of walking. Analyzing data from over 500 trials with healthy subjects and Parkinson’s patients yields a correct-pairing success rate of 99.8% after 3 s and 100% after 5 s.

Elements of Gait Kinematic Analyze in CAMONAL Project

2013 ◽  
Vol 436 ◽  
pp. 247-254
Author(s):  
Mihai Berteanu ◽  
Pierre de Hillerin ◽  
Radu Bidiugan ◽  
Ileana Ciobanu ◽  
Ruxandra Badea ◽  
...  
Keyword(s):  
Human Body ◽  
Lower Limb ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Rehabilitation Medicine ◽  
Sensor Systems ◽  
Limb Movements ◽  
Nervous Control ◽  
The Way

The kinematics of the human body is very complex. Every movement involves many joints, muscles and a special nervous control. The modern miniaturized inertial sensor systems prove to be valuable tools for rehabilitation medicine. We present the way a system of inertial sensors can be used to compare healthy and affected lower limb movements during gait.

Examination of the reliability of an inertial sensor-based gait analysis system

2017 ◽  
Vol 62 (6) ◽  
pp. 615-622 ◽  
Author(s):  
Katja Orlowski ◽  
Falko Eckardt ◽  
Fabian Herold ◽  
Norman Aye ◽  
Jürgen Edelmann-Nusser ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Upper Body ◽  
Initial Contact ◽  
Gait Parameters ◽  
Analysis System ◽  
Almost All

AbstractGait analysis is an important and useful part of the daily therapeutic routine. InvestiGAIT, an inertial sensor-based system, was developed for using in different research projects with a changing number and position of sensors and because commercial systems do not capture the motion of the upper body. The current study is designed to evaluate the reliability of InvestiGAIT consisting of four off-the-shelf inertial sensors and in-house capturing and analysis software. Besides the determination of standard gait parameters, the motion of the upper body (pelvis and spine) can be investigated. Kinematic data of 25 healthy individuals (age: 25.6±3.3 years) were collected using a test-retest design with 1 week between measurement sessions. We calculated different parameters for absolute [e.g. limits of agreement (LoA)] and relative reliability [intraclass correlation coefficients (ICC)]. Our results show excellent ICC values for most of the gait parameters. Midswing height (MH), height difference (HD) of initial contact (IC) and terminal contact (TC) and stride length (SL) are the gait parameters, which did not exhibit acceptable values representing absolute reliability. Moreover, the parameters derived from the motion of the upper body (pelvis and spine) show excellent ICC values or high correlations. Our results indicate that InvestiGAIT is suitable for reliable measurement of almost all the considered gait parameters.

scholarly journals Reference in-vitro dataset for inertial-sensor-to-bone alignment applied to the tibiofemoral joint

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ive Weygers ◽  
Manon Kok ◽  
Thomas Seel ◽  
Darshan Shah ◽  
Orçun Taylan ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Ground Truth ◽  
Transformation Matrices ◽  
True Model ◽  
Advance Research ◽  
Manual Palpation ◽  
Visualization Tools ◽  
Inertial Measurements

AbstractSkin-attached inertial sensors are increasingly used for kinematic analysis. However, their ability to measure outside-lab can only be exploited after correctly aligning the sensor axes with the underlying anatomical axes. Emerging model-based inertial-sensor-to-bone alignment methods relate inertial measurements with a model of the joint to overcome calibration movements and sensor placement assumptions. It is unclear how good such alignment methods can identify the anatomical axes. Any misalignment results in kinematic cross-talk errors, which makes model validation and the interpretation of the resulting kinematics measurements challenging. This study provides an anatomically correct ground-truth reference dataset from dynamic motions on a cadaver. In contrast with existing references, this enables a true model evaluation that overcomes influences from soft-tissue artifacts, orientation and manual palpation errors. This dataset comprises extensive dynamic movements that are recorded with multimodal measurements including trajectories of optical and virtual (via computed tomography) anatomical markers, reference kinematics, inertial measurements, transformation matrices and visualization tools. The dataset can be used either as a ground-truth reference or to advance research in inertial-sensor-to-bone-alignment.

scholarly journals Smartphone-Based Inertial Odometry for Blind Walkers

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4033
Author(s):  
Peng Ren ◽  
Fatemeh Elyasi ◽  
Roberto Manduchi
Keyword(s):  
Particle Filtering ◽  
Inertial Sensors ◽  
State Of The Art ◽  
Inertial Sensor ◽  
Mean Shift ◽  
Gait Pattern ◽  
Comparative Assessment ◽  
Pedestrian Tracking ◽  
Step Counter ◽  
Assisted Navigation

Pedestrian tracking systems implemented in regular smartphones may provide a convenient mechanism for wayfinding and backtracking for people who are blind. However, virtually all existing studies only considered sighted participants, whose gait pattern may be different from that of blind walkers using a long cane or a dog guide. In this contribution, we present a comparative assessment of several algorithms using inertial sensors for pedestrian tracking, as applied to data from WeAllWalk, the only published inertial sensor dataset collected indoors from blind walkers. We consider two situations of interest. In the first situation, a map of the building is not available, in which case we assume that users walk in a network of corridors intersecting at 45° or 90°. We propose a new two-stage turn detector that, combined with an LSTM-based step counter, can robustly reconstruct the path traversed. We compare this with RoNIN, a state-of-the-art algorithm based on deep learning. In the second situation, a map is available, which provides a strong prior on the possible trajectories. For these situations, we experiment with particle filtering, with an additional clustering stage based on mean shift. Our results highlight the importance of training and testing inertial odometry systems for assisted navigation with data from blind walkers.

scholarly journals On Expressive Features for Gait Analysis using Lower Limb Inertial Sensor Data

2020 ◽  
Vol 53 (2) ◽  
pp. 15990-15997
Author(s):  
Felix Laufer ◽  
Michael Lorenz ◽  
Bertram Taetz ◽  
Gabriele Bleser
Keyword(s):  
Gait Analysis ◽  
Lower Limb ◽  
Inertial Sensor ◽  
Sensor Data

scholarly journals Inertial Sensor Reliability and Validity for Static and Dynamic Balance in Healthy Adults: A Systematic Review

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5167
Author(s):  
Nicky Baker ◽  
Claire Gough ◽  
Susan J. Gordon
Keyword(s):  
Systematic Review ◽  
Discriminant Validity ◽  
Inertial Sensors ◽  
Postural Sway ◽  
Inertial Sensor ◽  
Dynamic Balance ◽  
Healthy Adults ◽  
Gait Velocity ◽  
Time Data ◽  
Targeted Interventions

Compared to laboratory equipment inertial sensors are inexpensive and portable, permitting the measurement of postural sway and balance to be conducted in any setting. This systematic review investigated the inter-sensor and test-retest reliability, and concurrent and discriminant validity to measure static and dynamic balance in healthy adults. Medline, PubMed, Embase, Scopus, CINAHL, and Web of Science were searched to January 2021. Nineteen studies met the inclusion criteria. Meta-analysis was possible for reliability studies only and it was found that inertial sensors are reliable to measure static standing eyes open. A synthesis of the included studies shows moderate to good reliability for dynamic balance. Concurrent validity is moderate for both static and dynamic balance. Sensors discriminate old from young adults by amplitude of mediolateral sway, gait velocity, step length, and turn speed. Fallers are discriminated from non-fallers by sensor measures during walking, stepping, and sit to stand. The accuracy of discrimination is unable to be determined conclusively. Using inertial sensors to measure postural sway in healthy adults provides real-time data collected in the natural environment and enables discrimination between fallers and non-fallers. The ability of inertial sensors to identify differences in postural sway components related to altered performance in clinical tests can inform targeted interventions for the prevention of falls and near falls.

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

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2869
Author(s):  
Jiaen Wu ◽  
Kiran Kuruvithadam ◽  
Alessandro Schaer ◽  
Richie Stoneham ◽  
George Chatzipirpiridis ◽  
...  
Keyword(s):  
Real Time ◽  
Inertial Sensor ◽  
Neurological Diseases ◽  
Principal Component ◽  
Sampling Frequency ◽  
Powerful Analytical Tool ◽  
Optical Motion ◽  
Analysis System ◽  
Optical Motion Capture ◽  
Gait Monitoring

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.

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