170 research outputs found
Centre of pressure estimation during walking using only inertial-measurement units and end-to-end statistical modelling
Estimation of the centre of pressure (COP) is an important part of the gait
analysis, for example, when evaluating the functional capacity of individuals
affected by motor impairment. Inertial measurement units (IMUs) and force
sensors are commonly used to measure gait characteristic of healthy and
impaired subjects. We present a methodology for estimating the COP solely from
raw gyroscope, accelerometer, and magnetometer data from IMUs using statistical
modelling. We demonstrate the viability of the method using an example of two
models: a linear model and a non-linear Long-Short-Term Memory (LSTM) neural
network model. Models were trained on the COP ground truth data measured using
an instrumented treadmill and achieved the average intra-subject root mean
square (RMS) error between estimated and ground truth COP of 12.3mm and the
average inter-subject RMS error of 23.7mm which is comparable or better than
similar studies so far. We show that the calibration procedure in the
instrumented treadmill can be as short as a couple of minutes without the
decrease in our model performance. We also show that the magnetic component of
the recorded IMU signal, which is most sensitive to environmental changes, can
be safely dropped without a significant decrease in model performance. Finally,
we show that the number of IMUs can be reduced to five without deterioration in
the model performance.Comment: 21 page
Simplified markerless stride detection pipeline (sMaSDP) for surface EMG segmentation
To diagnose mobility impairments and select appropriate physiotherapy, gait assessment studies are often recommended. These studies are usually conducted in confined clinical settings, which may feel foreign to a subject and affect their motivation, coordination, and overall mobility. Conducting gait studies in unconstrained natural settings instead, such as the subject’s Activities of Daily Life (ADL), could provide a more accurate assessment. To appropriately diagnose gait deficiencies, muscle activity should be recorded in parallel with typical kinematic studies. To achieve this, Electromyography (EMG) and kinematic are collected synchronously. Our protocol sMaSDP introduces a simplified markerless gait event detection pipeline for the segmentation of EMG signals via Inertial Measurement Unit (IMU) data, based on a publicly available dataset. This methodology intends to provide a simple, detailed sequence of processing steps for gait event detection via IMU and EMG, and serves as tutorial for beginners in unconstrained gait assessment studies. In an unconstrained gait experiment, 10 healthy subjects walk through a course designed to mimic everyday walking, with their kinematic and EMG data recorded, for a total of 20 trials. Five different walking modalities, such as level walking, ramp up/down, and staircase up/down are included. By segmenting and filtering the data, we generate an algorithm that detects heel-strike events, using a single IMU, and isolates EMG activity of gait cycles. Applicable to different datasets, sMaSDP was tested in healthy gait and gait data of Parkinson’s Disease (PD) patients. Using sMaSDP, we extracted muscle activity in healthy walking and identified heel-strike events in PD patient data. The algorithm parameters, such as expected velocity and cadence, are adjustable and can further improve the detection accuracy, and our emphasis on the wearable technologies makes this solution ideal for ADL gait studies
Atypical Gait Cycles in Parkinson’s Disease
It is important to find objective biomarkers for evaluating gait in Parkinson’s Disease (PD), especially related to the foot and lower leg segments. Foot-switch signals, analyzed through Statistical Gait Analysis (SGA), allow the foot-floor contact sequence to be characterized during a walking session lasting five-minutes, which includes turnings. Gait parameters were compared between 20 PD patients and 20 age-matched controls. PDs showed similar straight-line speed, cadence, and double-support compared to controls, as well as typical gait-phase durations, except for a small decrease in the flat-foot contact duration (−4% of the gait cycle, p = 0.04). However, they showed a significant increase in atypical gait cycles (+42%, p = 0.006), during both walking straight and turning. A forefoot strike, instead of a “normal” heel strike, characterized the large majority of PD’s atypical cycles, whose total percentage was 25.4% on the most-affected and 15.5% on the least-affected side. Moreover, we found a strong correlation between the atypical cycles and the motor clinical score UPDRS-III (r = 0.91, p = 0.002), in the subset of PD patients showing an abnormal number of atypical cycles, while we found a moderate correlation (r = 0.60, p = 0.005), considering the whole PD population. Atypical cycles have proved to be a valid biomarker to quantify subtle gait dysfunctions in PD patients
Stretch sensors for measuring knee kinematics in sports
The popularity of wearable technology in sport has increased, due to its ability to provide unobtrusive
monitoring of athletes. This technology has been used to objectively measure kinetic and kinematic
variables, with the aim of preventing injury, maximising athletic performance and classifying the skill
level of athletes, all of which can influence training and coaching practices. Wearable technologies
overcome the limitations of motion capture systems which are limited in their capture volume, enabling
the collection of data in-field, during training and competition. Inertial sensors are a common form of
technology used in these environments however, their high-cost and complex calibration due to multiple
sensor integration can make them prohibitive for widespread use.
This thesis focuses on the development of a strain sensor that can be used to measure knee range
of motion in sports, specifically rowing and cycling, as a potential low-cost, lightweight alternative to
inertial sensors which can also be integrated into clothing, making them more discreet. A systematic
review highlighted the lack of alternate technologies to inertial sensors such as strain sensors, as well
as the limited use of wearable technologies in both rowing and cycling.
Strain sensors were fabricated from a carbon nanotube-natural rubber composite using solvent exchange techniques and employed a piezoresistive sensing mechanism. These were then characterised
using mechanical testing, to determine their electrical properties under cyclical strain. The strain sensors displayed hysteretic behaviour, but were durable, withstanding over 4500 strain cycles. Statistical
analysis indicated that over 60% of the tests conducted had good intra-test variability with regards to
the resistance response range in each strain cycle and sensor response deviating by less than 10% at
strain rates below 100 mm/min and less than 20% at a strain rate of 350 mm/min.
These sensors were integrated into a wearable sensor system and tested on rowing and cycling
cohorts consisting of ten athletes each, to assess the translational use of the strain sensor. This
preliminary testing indicated that strain sensors were able to track the motion of the knee during the
rowing stroke and cycling pedalling motion, when compared to the output of a motion capture system.
Perspectives of participants on the wearable system were collected, which indicated their desire for a
system that they could use in their sport, and they considered the translation of this system for real-life
use with further development to improve comfort of the system and consistency of the sensor response.
The strain sensors developed in this project, when integrated into a wearable sensor system, have the
potential to provide an unobtrusive method of measuring knee kinematics, helping athletes, coaches
and other support staff make technical changes that can reduce injury risk and improve performance.Open Acces
Modern facilities for experimental measurement of dynamic loads induced by humans: a literature review.
This paper provides a critical overview of available technology and facilities for determining human-induced dynamic forces of civil engineering structures, such as due to walking, running, jumping and bouncing. In addition to traditional equipment for direct force measurements comprising force plate(s), foot pressure insoles and instrumented treadmills, the review also investigates possibility of using optical motion tracking systems (marker-based and marker-free optoelectronic technology) and non-optical motion tracking systems (inertial sensors) to reproduce contact forces between humans and structures based on body kinematics data and known body mass distribution. Although significant technological advancements have been made in the last decade, the literature survey showed that the state-of-the-art force measurements are often limited to individuals in artificial laboratory environments. Experimental identification of seriously needed group- and crowd-induced force data recorded on as-built structures, such as footbridges, grandstands and floors, still remains a challenge due to the complexity of human actions and the lack of adequate equipment
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