scholarly journals An Inertial Sensor-Based Gait Analysis Pipeline for the Assessment of Real-World Stair Ambulation Parameters

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6559
Author(s):  
Nils Roth ◽  
Arne Küderle ◽  
Dominik Prossel ◽  
Heiko Gassner ◽  
Bjoern M. Eskofier ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Real World ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Analysis Pipeline ◽  
Postural Control System ◽  
Mobility Impairments ◽  
Spatial Features ◽  
Level Walking ◽  
Stair Ambulation

Climbing stairs is a fundamental part of daily life, adding additional demands on the postural control system compared to level walking. Although real-world gait analysis studies likely contain stair ambulation sequences, algorithms dedicated to the analysis of such activities are still missing. Therefore, we propose a new gait analysis pipeline for foot-worn inertial sensors, which can segment, parametrize, and classify strides from continuous gait sequences that include level walking, stair ascending, and stair descending. For segmentation, an existing approach based on the hidden Markov model and a feature-based gait event detection were extended, reaching an average segmentation F1 score of 98.5% and gait event timing errors below ±10ms for all conditions. Stride types were classified with an accuracy of 98.2% using spatial features derived from a Kalman filter-based trajectory reconstruction. The evaluation was performed on a dataset of 20 healthy participants walking on three different staircases at different speeds. The entire pipeline was additionally validated end-to-end on an independent dataset of 13 Parkinson’s disease patients. The presented work aims to extend real-world gait analysis by including stair ambulation parameters in order to gain new insights into mobility impairments that can be linked to clinically relevant conditions such as a patient’s fall risk and disease state or progression.

scholarly journals Re-Enactment as a Method to Reproduce Real-World Fall Events Using Inertial Sensor Data: Development and Usability Study

10.2196/13961 ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. e13961
Author(s):  
Kim Sarah Sczuka ◽  
Lars Schwickert ◽  
Clemens Becker ◽  
Jochen Klenk
Keyword(s):  
Real World ◽  
Independent Living ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Center Of Mass ◽  
Fall Detection ◽  
Video Data ◽  
Sensor Data ◽  
Movement Behavior ◽  
The Real

Background Falls are a common health problem, which in the worst cases can lead to death. To develop reliable fall detection algorithms as well as suitable prevention interventions, it is important to understand circumstances and characteristics of real-world fall events. Although falls are common, they are seldom observed, and reports are often biased. Wearable inertial sensors provide an objective approach to capture real-world fall signals. However, it is difficult to directly derive visualization and interpretation of body movements from the fall signals, and corresponding video data is rarely available. Objective The re-enactment method uses available information from inertial sensors to simulate fall events, replicate the data, validate the simulation, and thereby enable a more precise description of the fall event. The aim of this paper is to describe this method and demonstrate the validity of the re-enactment approach. Methods Real-world fall data, measured by inertial sensors attached to the lower back, were selected from the Fall Repository for the Design of Smart and Self-Adaptive Environments Prolonging Independent Living (FARSEEING) database. We focused on well-described fall events such as stumbling to be re-enacted under safe conditions in a laboratory setting. For the purposes of exemplification, we selected the acceleration signal of one fall event to establish a detailed simulation protocol based on identified postures and trunk movement sequences. The subsequent re-enactment experiments were recorded with comparable inertial sensor configurations as well as synchronized video cameras to analyze the movement behavior in detail. The re-enacted sensor signals were then compared with the real-world signals to adapt the protocol and repeat the re-enactment method if necessary. The similarity between the simulated and the real-world fall signals was analyzed with a dynamic time warping algorithm, which enables the comparison of two temporal sequences varying in speed and timing. Results A fall example from the FARSEEING database was used to show the feasibility of producing a similar sensor signal with the re-enactment method. Although fall events were heterogeneous concerning chronological sequence and curve progression, it was possible to reproduce a good approximation of the motion of a person’s center of mass during fall events based on the available sensor information. Conclusions Re-enactment is a promising method to understand and visualize the biomechanics of inertial sensor-recorded real-world falls when performed in a suitable setup, especially if video data is not available.

scholarly journals Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis—A Scoping Review

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7143 ◽  
Author(s):  
Dylan Kobsar ◽  
Zaryan Masood ◽  
Heba Khan ◽  
Noha Khalil ◽  
Marium Yossri Kiwan ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Scoping Review ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Linear Acceleration ◽  
Patient Specific ◽  
Common Location ◽  
Spatiotemporal Parameters ◽  
Wearable Inertial Sensors ◽  
Study Designs

Our objective was to conduct a scoping review which summarizes the growing body of literature using wearable inertial sensors for gait analysis in lower limb osteoarthritis. We searched six databases using predetermined search terms which highlighted the broad areas of inertial sensors, gait, and osteoarthritis. Two authors independently conducted title and abstract reviews, followed by two authors independently completing full-text screenings. Study quality was also assessed by two independent raters and data were extracted by one reviewer in areas such as study design, osteoarthritis sample, protocols, and inertial sensor outcomes. A total of 72 articles were included, which studied the gait of 2159 adults with osteoarthritis (OA) using inertial sensors. The most common location of OA studied was the knee (n = 46), followed by the hip (n = 22), and the ankle (n = 7). The back (n = 41) and the shank (n = 40) were the most common placements for inertial sensors. The three most prevalent biomechanical outcomes studied were: mean spatiotemporal parameters (n = 45), segment or joint angles (n = 33), and linear acceleration magnitudes (n = 22). Our findings demonstrate exceptional growth in this field in the last 5 years. Nevertheless, there remains a need for more longitudinal study designs, patient-specific models, free-living assessments, and a push for “Code Reuse” to maximize the unique capabilities of these devices and ultimately improve how we diagnose and treat this debilitating disease.

The Effect of Vertical and Multiplanar Accelerations on Differing Production Seat Belt Sensor Designs

2008 ◽  
Author(s):  
Steven E. Meyer ◽  
Arin A. Oliver ◽  
Davis A. Hock ◽  
Joshua D. Hayden ◽  
Stephen M. Forrest ◽  
...  
Keyword(s):  
Real World ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Seat Belt ◽  
Design Feature ◽  
Test Methods ◽  
Center Of Gravity ◽  
Restraint System ◽  
Occupant Injury ◽  
Cup Design

In real world accidents, vehicles are often subjected to multiplanar crash environments such as those seen in multiple impacts or rollover collisions. These various impact environments can directly affect the accelerations experienced by the vehicle and its components. Particularly vulnerable is the vehicle sensitive (inertially activated) crash sensor that is commonly used in seat belt retractor design. Though these designs have been proven effective in the frontal crash environments, they have also been shown to be susceptible to unlocking and/or a delay in lockup by vertical and/or rotational accelerations [1–3]. Such delayed retractor response can result in belt webbing payout and significant occupant motion within the restraint system. This can increase the likelihood and severity of occupant injury within the vehicle and/or injury from partial or full ejection in rollovers. Occurrence of this phenomenon in the real world has been documented and previously published [4, 5]. Evidence of retractor sensor unlocking and resulting spoolout includes identifiable forensic markings on the restraint system components [6] as well as occupant excursion evidence including full ejection and excessive partial ejection of properly belted vehicle occupants. The subject paper will follow previously published test methodologies [1–3] and report on new testing conducted on four differing production seat belt retractor designs. The test methods include linear accelerator tests to document the effects of a vertical pulse on an inertially sensitive retractor and, secondly, rotational accelerator tests wherein the retractors were mounted on a rotating fixture and subjected to various roll rates. Two tested retractors are the ball and cup design with the retractor’s inertial sensor incorporated within the retractor mounted in the vehicle’s roof pillars. The other two tested retractor designs utilize remote mounted (near the vehicle’s center of gravity) inertial sensors. These retractors are biased to the locked position via a spring and are held unlocked only when a solenoid is energized by the vehicle’s remote inertial sensor. For the rotational acceleration tests, the retractors were mounted in similar positions to that seen in their respective production vehicles relative to the vehicle’s center of gravity. Two retractors were then tested concurrently on the same test apparatus such that they were subjected to equivalent multiplanar environments, and thus allowed for direct comparison of their retractor design sensitivities and lockup performance. Additionally, a vehicle pillar mounted retractor was tested with and without a webbing sensitive lockup feature to allow for analysis of this design feature in varying multiplanar environments. The retractors’ performance under both the linear/vertical and rollover test conditions were noticeably different and are analyzed in detail.

Detection of Gait Event and Supporting Leg during Overground Walking with Mediolateral Swing Angle

2011 ◽  
Vol 145 ◽  
pp. 567-573 ◽  
Author(s):  
Je Nam Kim ◽  
Mun Ho Ryu ◽  
Yoon Seok Yang ◽  
Seong Hyun Kim
Keyword(s):  
Gait Analysis ◽  
Motion Tracking ◽  
Microelectromechanical Systems ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Tracking System ◽  
Reference Method ◽  
Vertical Acceleration ◽  
Elastic Band ◽  
Swing Angle

Walking is one of the basic human activities. Several well-defined, motion tracking systems have been used for gait analysis. However, these systems such as the optical motion tracking system are very expensive and limited to laboratory usage. Recently, microelectromechanical systems (MEMS)-based inertial sensors have made it possible to overcome these disadvantages. The aim of this study was to identify gait events and the supporting leg by measuring the mediolateral swing angle. An inertial sensor unit with a 3-axis accelerometer and 2-axis gyroscope was attached to the subject’s lower trunk using an elastic band. Five, healthy and young (20–29 yrs.) subjects participated in this experiment. Each walked twice along a straight, 25-m path at three different speeds. During each trial, the sensor transmitted signals to a PC via Bluetooth technology. In this study, gait events and the supporting leg were identified using the peak and sign of the mediolateral swing angle. The mediolateral swing angle was calculated using the integrated gyroscope signal. For comparison, a well-defined spatiotemporal gait analysis technique was also applied. In this reference method, the gait event was identified with the last peak of the vertical acceleration before the sign change from positive to negative. The supporting leg was identified using the sign of the mediolateral acceleration double integration. Identification of the supporting leg was difficult in the reference method because of the offset and gravity components in the mediolateral acceleration. However, the proposed method reported here, showed stable identification of gait events and the supporting leg. This study could be expanded to more detailed gait analysis with the additional fusion of a 3-axis acceleration, gyroscope and magnetometer.

scholarly journals Computation of spatio-temporal parameters in level walking using a single inertial system in lean and obese adolescents

2017 ◽  
Vol 62 (5) ◽  
Author(s):  
Veronica Cimolin ◽  
Paolo Capodaglio ◽  
Nicola Cau ◽  
Manuela Galli ◽  
Cristina Santovito ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Inertial Sensor ◽  
Normal Weight ◽  
Inertial System ◽  
Obese Adolescents ◽  
Temporal Parameters ◽  
Lower Trunk ◽  
Level Walking ◽  
Spatio Temporal ◽  
Instrumented Gait Analysis

AbstractIn recent years, the availability of low-cost equipment capable of recording kinematic data during walking has facilitated the outdoor assessment of gait parameters, thus overcoming the limitations of three-dimensional instrumented gait analysis (3D-GA). The aim of this study is twofold: firstly, to investigate whether a single sensor on the lower trunk could provide valid spatio-temporal parameters in level walking in normal-weight and obese adolescents compared to instrumented gait analysis (GA); secondly, to investigate whether the inertial sensor is capable of capturing the spatio-temporal features of obese adolescent gait. These were assessed in 10 obese and 8 non-obese adolescents using both a single inertial sensor on the lower trunk and an optoelectronic system. The parameters obtained were not statistically different in either normal-weight or obese participants between the two methods. Obese adolescents walked with longer stance and double support phase compared to normal-weight participants. The results showed that the inertial system is a valid means of evaluating spatio-temporal parameters in obese individuals.

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 A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis Using Inertial Sensors

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7517
Author(s):  
Vânia Guimarães ◽  
Inês Sousa ◽  
Miguel Velhote Correia
Keyword(s):  
Gait Analysis ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Sensor Data ◽  
Coordinate Frame ◽  
Complete Evaluation ◽  
Gait Parameters ◽  
Deep Recurrent Neural Network ◽  
Optical Motion ◽  
Frame Transformation

Gait performance is an important marker of motor and cognitive decline in older adults. An instrumented gait analysis resorting to inertial sensors allows the complete evaluation of spatiotemporal gait parameters, offering an alternative to laboratory-based assessments. To estimate gait parameters, foot trajectories are typically obtained by integrating acceleration two times. However, to deal with cumulative integration errors, additional error handling strategies are required. In this study, we propose an alternative approach based on a deep recurrent neural network to estimate heel and toe trajectories. We propose a coordinate frame transformation for stride trajectories that eliminates the dependency from previous strides and external inputs. Predicted trajectories are used to estimate an extensive set of spatiotemporal gait parameters. We evaluate the results in a dataset comprising foot-worn inertial sensor data acquired from a group of young adults, using an optical motion capture system as a reference. Heel and toe trajectories are predicted with low errors, in line with reference trajectories. A good agreement is also achieved between the reference and estimated gait parameters, in particular when turning strides are excluded from the analysis. The performance of the method is shown to be robust to imperfect sensor-foot alignment conditions.

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
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