Extracting human gait signatures by body segment properties

AbstractThe purpose of this study was to investigate how the ball position along the mediolateral (M-L) direction of a golfer causes a chain effect in the ground reaction force, body segment and joint angles, and whole-body centre of mass during the golf swing. Twenty professional golfers were asked to complete five straight shots for each 5 different ball positions along M-L: 4.27 cm (ball diameter), 2.14 cm (ball radius), 0 cm (reference position at preferred ball position), – 2.14 cm, and – 4.27 cm, while their ground reaction force and body segment motions were captured. The dependant variables were calculated at 14 swing events from address to impact, and the differences between the ball positions were evaluated using Statistical Parametric Mapping. The left-sided ball positions at address showed a greater weight distribution on the left foot with a more open shoulder angle compared to the reference ball position, whereas the trend was reversed for the right-sided ball positions. These trends disappeared during the backswing and reappeared during the downswing. The whole-body centre of mass was also located towards the target for the left-sided ball positions throughout the golf swing compared to the reference ball position, whereas the trend was reversed for the right-sided ball positions. We have concluded that initial ball position at address can cause a series of chain effects throughout the golf swing.

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Human gait analysis based on OpenSim

2020 International Conference on Advanced Mechatronic Systems (ICAMechS) ◽

10.1109/icamechs49982.2020.9310111 ◽

2020 ◽

Author(s):

Jun Yu ◽

Shuaishuai Zhang ◽

Aihui Wang ◽

Wei Li

Keyword(s):

Gait Analysis ◽

Human Gait ◽

Human Gait Analysis

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Validation method for body-mounted sensor attachments

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-2006 ◽

2020 ◽

Vol 6 (2) ◽

Author(s):

Katharina Schmidt ◽

David Hochmann

Keyword(s):

Evaluation Method ◽

Tracking System ◽

The Body ◽

Exploratory Research ◽

Body Segment ◽

Validation Method ◽

Sensor Position ◽

Validation Parameters ◽

Measurement Units ◽

Soft Tissue Artifact

AbstractSmall sensor devices like inertial measurement units enable mobile movement and gait analysis, whereby existing systems differ in data acquisition, data processing, and gait parameter calculation. Concerning the validation, recent studies focus on the captured motion and the influence of sensor positioning with respect to the accuracy of the computed biomechanical parameters in comparison to a reference system. Although soft tissue artifact is a major source of error for skin-mounted sensors, there are no investigations regarding the relative movement between the body segment and sensor attachment itself. The aim of this study is to find an evaluation method and to determine parameters that allow the validation of various sensor attachment types and different sensor positionings. The analysis includes the comparison between an adhesive and strap attachment variant as well as the frontal and lateral sensor placement. To validate different attachments, an optical marker-based tracking system was used to measure the body segment and sensor position during movement. The distance between these two positions was calculated and analyzed to determine suitable validation parameters. Despite the exploratory research, the results suggest a feasible validation method to detect differences between the attachments, independent of the sensor type. To have representative and statistically validated results, further studies that involve more participants are necessary.

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Variability of human gait: Long-range autocorrelations and fluctuation magnitude of stride duration

Annals of Physical and Rehabilitation Medicine ◽

10.1016/j.rehab.2014.03.1557 ◽

2014 ◽

Vol 57 ◽

pp. e427-e428

Author(s):

B. Bollens ◽

C. Detrembleur ◽

F. Crevecoeur ◽

T. Lejeune

Keyword(s):

Long Range ◽

Human Gait ◽

Stride Duration

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A phase-shifting based human gait phase estimation for powered transfemoral prostheses

IEEE Robotics and Automation Letters ◽

10.1109/lra.2021.3068907 ◽

2021 ◽

pp. 1-1

Author(s):

Woolim Hong ◽

Namita Anil Kumar ◽

Pilwon Hur

Keyword(s):

Phase Shifting ◽

Human Gait ◽

Phase Estimation ◽

Gait Phase

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Robust Step Detection from Different Waist-Worn Sensor Positions: Implications for Clinical Studies

Digital Biomarkers ◽

10.1159/000511611 ◽

2020 ◽

Vol 4 (1) ◽

pp. 50-58

Author(s):

Matthias Tietsch ◽

Amir Muaremi ◽

Ieuan Clay ◽

Felix Kluge ◽

Holger Hoefling ◽

...

Keyword(s):

Clinical Study ◽

Clinical Studies ◽

Neurological Diseases ◽

Human Gait ◽

Robust Solution ◽

Step Detection ◽

Study Protocols ◽

Study Results ◽

Wide Range ◽

The Impact

Analyzing human gait with inertial sensors provides valuable insights into a wide range of health impairments, including many musculoskeletal and neurological diseases. A representative and reliable assessment of gait requires continuous monitoring over long periods and ideally takes place in the subjects’ habitual environment (real-world). An inconsistent sensor wearing position can affect gait characterization and influence clinical study results, thus clinical study protocols are typically highly proscriptive, instructing all participants to wear the sensor in a uniform manner. This restrictive approach improves data quality but reduces overall adherence. In this work, we analyze the impact of altering the sensor wearing position around the waist on sensor signal and step detection. We demonstrate that an asymmetrically worn sensor leads to additional odd-harmonic frequency components in the frequency spectrum. We propose a robust solution for step detection based on autocorrelation to overcome sensor position variation (sensitivity = 0.99, precision = 0.99). The proposed solution reduces the impact of inconsistent sensor positioning on gait characterization in clinical studies, thus providing more flexibility to protocol implementation and more freedom to participants to wear the sensor in the position most comfortable to them. This work is a first step towards truly position-agnostic gait assessment in clinical settings.

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Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition

Sensors ◽

10.3390/s21030789 ◽

2021 ◽

Vol 21 (3) ◽

pp. 789

Author(s):

David Kreuzer ◽

Michael Munz

Keyword(s):

Gait Analysis ◽

Time Series Data ◽

Low Cost ◽

Analysis Data ◽

Gait Disorders ◽

Human Gait ◽

Series Data ◽

Ageing Society ◽

Time Motion ◽

Gait Phase

With an ageing society comes the increased prevalence of gait disorders. The restriction of mobility leads to a considerable reduction in the quality of life, because associated falls increase morbidity and mortality. Consideration of gait analysis data often alters surgical recommendations. For that reason, the early and systematic diagnostic treatment of gait disorders can spare a lot of suffering. As modern gait analysis systems are, in most cases, still very costly, many patients are not privileged enough to have access to comparable therapies. Low-cost systems such as inertial measurement units (IMUs) still pose major challenges, but offer possibilities for automatic real-time motion analysis. In this paper, we present a new approach to reliably detect human gait phases, using IMUs and machine learning methods. This approach should form the foundation of a new medical device to be used for gait analysis. A model is presented combining deep 2D-convolutional and LSTM networks to perform a classification task; it predicts the current gait phase with an accuracy of over 92% on an unseen subject, differentiating between five different phases. In the course of the paper, different approaches to optimize the performance of the model are presented and evaluated.

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