scholarly journals Analyzing Gait in the Real World Using Wearable Movement Sensors and Frequently Repeated Movement Paths

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1925 ◽  
Author(s):  
Weixin Wang ◽  
Peter Gabriel Adamczyk
Keyword(s):  
Statistical Power ◽  
Stride Length ◽  
Unsolved Problem ◽  
Inertial Sensor ◽  
Real Life ◽  
Movement Behavior ◽  
Gps Receiver ◽  
Athletic Shoes ◽  
Foot Clearance ◽  
Movement Sensors

Assessing interventions for mobility disorders using real-life movement remains an unsolved problem. We propose a new method combining the strengths of traditional laboratory studies where environment is strictly controlled, and field-based studies where subjects behave naturally. We use a foot-mounted inertial sensor, a GPS receiver and a barometric altitude sensor to reconstruct a subject’s path and detailed foot movement, both indoors and outdoors, during days-long measurement using strapdown navigation and sensor fusion algorithms. We cluster repeated movement paths based on location, and propose that on these paths, most environmental and behavioral factors (e.g., terrain and motivation) are as repeatable as in a laboratory. During each bout of movement along a frequently repeated path, any synchronized measurement can be isolated for study, enabling focused statistical comparison of different interventions. We conducted a 10-day test on one subject wearing athletic shoes and sandals each for five days. The algorithm detected four frequently-repeated straight walking paths with at least 300 total steps and repetitions on at least three days for each condition. Results on these frequently-repeated paths indicated significantly lower foot clearance and shorter stride length and a trend toward decreased stride width when wearing athletic shoes vs. sandals. Comparisons based on all straight walking were similar, showing greater statistical power, but higher variability in the data. The proposed method offers a new way to evaluate how mobility interventions affect everyday movement behavior.

scholarly journals Gait Variability and Complexity during Single and Dual-Task Walking on Different Surfaces in Outdoor Environment

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4792
Author(s):  
Denisa Nohelova ◽  
Lucia Bizovska ◽  
Nicolas Vuillerme ◽  
Zdenek Svoboda
Keyword(s):  
Dual Task ◽  
Inertial Sensor ◽  
Cognitive Task ◽  
Real Life ◽  
Gait Pattern ◽  
Gait Variability ◽  
Outdoor Environment ◽  
Manual Task ◽  
Lower Trunk ◽  
Trunk Acceleration

Nowadays, gait assessment in the real life environment is gaining more attention. Therefore, it is desirable to know how some factors, such as surfaces (natural, artificial) or dual-tasking, influence real life gait pattern. The aim of this study was to assess gait variability and gait complexity during single and dual-task walking on different surfaces in an outdoor environment. Twenty-nine healthy young adults aged 23.31 ± 2.26 years (18 females, 11 males) walked at their preferred walking speed on three different surfaces (asphalt, cobbles, grass) in single-task and in two dual-task conditions (manual task—carrying a cup filled with water, cognitive task—subtracting the number 7). A triaxial inertial sensor attached to the lower trunk was used to record trunk acceleration during gait. From 15 strides, sample entropy (SampEn) as an indicator of gait complexity and root mean square (RMS) as an indicator of gait variability were computed. The findings demonstrate that in an outdoor environment, the surfaces significantly impacted only gait variability, not complexity, and that the tasks affected both gait variability and complexity in young healthy adults.

Evaluation of Location System Combining a GPS Receiver with Inertial Sensor

1991 ◽  
Author(s):  
Hiroaki Tsuji ◽  
Hiroyuki Maeda ◽  
Akihito Shibata ◽  
Fuminori Morisue
Keyword(s):  
Inertial Sensor ◽  
Gps Receiver ◽  
Location System

scholarly journals DYSKIMOT: An ultra-low-cost inertial sensor to assess head’s rotational kinematics in adults during the Didren-Laser Test

2019 ◽  
Author(s):  
Renaud Hage ◽  
Christine Detrembleur ◽  
Frédéric Dierick ◽  
Laurent Pitance ◽  
Laurent Jojczyk ◽  
...  
Keyword(s):  
Time Series ◽  
Clinical Practice ◽  
Gold Standard ◽  
Inertial Sensor ◽  
Low Cost ◽  
Real Life ◽  
Optoelectronic System ◽  
Qualitative Level ◽  
Cervical Motion ◽  
Head Rotations

Various noninvasive measurement devices can be used to assess cervical motion. Size, complexity and cost of gold-standard systems make them not suited in clinical practice, and actually difficult to use outside dedicated laboratory. Nowadays, ultra-low-cost inertial measurement units are available but without any packaging nor user-friendly interface. DYSKIMOT is a home- designed, small-sized, motion sensor based on the latter technology, aiming at being used by clinicians in “real-life situations”. In the present study. DYSKIMOT was compared with a gold- standard optoelectronic system (Elite). Our goal was to evaluate the accuracy of DYSKIMOT in assessing the kinematics in fast head rotations. Kinematics was simultaneously recorded by the DYSKIMOT and Elite systems during the execution of the DidRen Laser test and performed by 15 participants and 9 patients. Kinematic variables were computed from the position, speed and acceleration time series. Two-way ANOVA, Passing-Bablok regressions and Dynamic Time Warping analysis showed good to excellent agreement between Elite and DYSKIMOT, both at the qualitative level of the time series shape and at the quantitative level of peculiar kinematical events’ measured values. In conclusion, DYSKIMOT sensor is as relevant as a gold-standard system to assess kinematical features during fast head rotations in participants and patients, demonstrating its usefulness in clinical practice or research in ecological environment.

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.

FDI for Inertial Sensor using Single Antenna GPS Receiver

2004 ◽  
Vol 37 (6) ◽  
pp. 867-871
Author(s):  
Young Jin Kim ◽  
Youdan Kim ◽  
Chan Gook Park
Keyword(s):  
Inertial Sensor ◽  
Gps Receiver ◽  
Single Antenna

scholarly journals The Parkinsonian Gait Spatiotemporal Parameters Quantified by a Single Inertial Sensor before and after Automated Mechanical Peripheral Stimulation Treatment

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Ana Kleiner ◽  
Manuela Galli ◽  
Maria Gaglione ◽  
Daniela Hildebrand ◽  
Patrizio Sale ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Stride Length ◽  
Inertial Sensor ◽  
Gait Velocity ◽  
Control Group ◽  
Peripheral Stimulation ◽  
Before And After ◽  
Spatiotemporal Parameters ◽  
The One ◽  
One Way Anova

This study aims to evaluate the change in gait spatiotemporal parameters in subjects with Parkinson’s disease (PD) before and after Automated Mechanical Peripheral Stimulation (AMPS) treatment. Thirty-five subjects with PD and 35 healthy age-matched subjects took part in this study. A dedicated medical device (Gondola) was used to administer the AMPS. All patients with PD were treated in off levodopa phase and their gait performances were evaluated by an inertial measurement system before and after the intervention. The one-way ANOVA for repeated measures was performed to assess the differences between pre- and post-AMPS and the one-way ANOVA to assess the differences between PD patients and the control group. Spearman’s correlations assessed the associations between patients with PD clinical status (H&Y) and the percentage of improvement of the gait variables after AMPS (α<0.05for all tests). The PD group had an improvement of 14.85% in the stride length; 14.77% in the gait velocity; and 29.91% in the gait propulsion. The correlation results showed that the higher the H&Y classification, the higher the stride length percentage of improvement. The treatment based on AMPS intervention seems to induce a better performance in the gait pattern of PD patients, mainly in intermediate and advanced stages of the condition.

scholarly journals Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 840 ◽  
Author(s):  
Qu Wang ◽  
Langlang Ye ◽  
Haiyong Luo ◽  
Aidong Men ◽  
Fang Zhao ◽  
...  
Keyword(s):  
Error Rate ◽  
Stride Length ◽  
Inertial Sensor ◽  
Dead Reckoning ◽  
Training Data ◽  
Sensor Data ◽  
Walking Distance ◽  
Estimation Accuracy ◽  
Length Estimation ◽  
Pedestrian Dead Reckoning

Accurate stride-length estimation is a fundamental component in numerous applications, such as pedestrian dead reckoning, gait analysis, and human activity recognition. The existing stride-length estimation algorithms work relatively well in cases of walking a straight line at normal speed, but their error overgrows in complex scenes. Inaccurate walking-distance estimation leads to huge accumulative positioning errors of pedestrian dead reckoning. This paper proposes TapeLine, an adaptive stride-length estimation algorithm that automatically estimates a pedestrian’s stride-length and walking-distance using the low-cost inertial-sensor embedded in a smartphone. TapeLine consists of a Long Short-Term Memory module and Denoising Autoencoders that aim to sanitize the noise in raw inertial-sensor data. In addition to accelerometer and gyroscope readings during stride interval, extracted higher-level features based on excellent early studies were also fed to proposed network model for stride-length estimation. To train the model and evaluate its performance, we designed a platform to collect inertial-sensor measurements from a smartphone as training data, pedestrian step events, actual stride-length, and cumulative walking-distance from a foot-mounted inertial navigation system module as training labels at the same time. We conducted elaborate experiments to verify the performance of the proposed algorithm and compared it with the state-of-the-art SLE algorithms. The experimental results demonstrated that the proposed algorithm outperformed the existing methods and achieves good estimation accuracy, with a stride-length error rate of 4.63% and a walking-distance error rate of 1.43% using inertial-sensor embedded in smartphone without depending on any additional infrastructure or pre-collected database when a pedestrian is walking in both indoor and outdoor complex environments (stairs, spiral stairs, escalators and elevators) with natural motion patterns (fast walking, normal walking, slow walking, running, jumping).

scholarly journals Sensor Type, Axis, and Position-Based Fusion and Feature Selection for Multimodal Human Daily Activity Recognition in Wearable Body Sensor Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Abeer A. Badawi ◽  
Ahmad Al-Kabbany ◽  
Heba A. Shaban
Keyword(s):  
Feature Selection ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Real Life ◽  
Human Gait ◽  
Body Sensor Networks ◽  
Sequential Feature Selection ◽  
Wearable Inertial Sensors ◽  
Robust Systems ◽  
Remote Health

This research addresses the challenge of recognizing human daily activities using surface electromyography (sEMG) and wearable inertial sensors. Effective and efficient recognition in this context has emerged as a cornerstone in robust remote health monitoring systems, among other applications. We propose a novel pipeline that can attain state-of-the-art recognition accuracies on a recent-and-standard dataset—the Human Gait Database (HuGaDB). Using wearable gyroscopes, accelerometers, and electromyography sensors placed on the thigh, shin, and foot, we developed an approach that jointly performs sensor fusion and feature selection. Being done jointly, the proposed pipeline empowers the learned model to benefit from the interaction of features that might have been dropped otherwise. Using statistical and time-based features from heterogeneous signals of the aforementioned sensor types, our approach attains a mean accuracy of 99.8%, which is the highest accuracy on HuGaDB in the literature. This research underlines the potential of incorporating EMG signals especially when fusion and selection are done simultaneously. Meanwhile, it is valid even with simple off-the-shelf feature selection methods such the Sequential Feature Selection family of algorithms. Moreover, through extensive simulations, we show that the left thigh is a key placement for attaining high accuracies. With one inertial sensor on that single placement alone, we were able to achieve a mean accuracy of 98.4%. The presented in-depth comparative analysis shows the influence that every sensor type, position, and placement can have on the attained recognition accuracies—a tool that can facilitate the development of robust systems, customized to specific scenarios and real-life applications.

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