ON THE USE OF INERTIAL SENSORS TO DETERMINE TRUNK DISPLACEMENT DURING WALKING: A VALIDATION STUDY

The aim of this study was to determine the accuracy trunk displacement extracted from an inertial motion capture system (IMC) compared to an optical motion capture system (OMC). Participants walked overground while trunk displacement data from IMC and OMC were simultaneously recorded. The resultant trunk speed from both systems during walking and brief standing periods were compared. No differences were found in trunk speed during walking between IMC and OMC. However, trunk speed was greater for the IMC during the transition periods when compared to the OMC (p\u3c0.05). It is concluded that trunk kinematic parameters extracted from IMCs have fair accuracy when compared to a gold standard during walking, but accuracy is reduced and speed is overestimated when recording kinematics around stationary periods

Human Gait Model Development for Objective Analysis of Pre/Post Gait Characteristics Following Lumbar Spine Surgery

Although multiple advanced tools and methods are available for gait analysis, the gait and its related disorders are usually assessed by visual inspection in the clinical environment. This thesis aims to introduce a gait analysis system that provides an objective method for gait evaluation in clinics and overcomes the limitations of the current gait analysis systems. Early identification of foot drop, a common gait disorder, would become possible using the proposed methodology

Contextualisation of running power: a systematic review

Problem Statement: Power meters have helped performance cyclists to revolutionisetheir training and competitions. However, running power is not obtained by a power meter, as in cycling, but is estimated through accelerometers, gyroscopes or inertial measurements units. Therefore, this relatively new concept must be correctly contextualised. Approach:The most widely used deviceis the summitmodel of the Stryd Running Power Meter, butthe validity, reliability and repeatability of this device must be studied extensively, both regarding the estimation of the running power and the biomechanical parameters. Purpose:The main purpose was to examine all articles where the Stryd device was used to analyse both running power and biomechanical parameters. Methods: Electronic databases were searched using key related terminology such as:Stryd, running power and biomechanical parameters. Results: The production of portable and low-cost equipmenthas led to the capacity toanalyse power and biomechanical parameters in running using different devices. Nevertheless, to avoid erroneous conclusions, it is necessary to take into account considerations in the different studies such as the device used, its placement and the level of the participantsunder study.Conclusions:The Stryd device could be considered as the most recommended device to measure running power compared to other available devices. Although the Stryd system could be a valid tool for measuring temporal parameters, RunScribe seems to be a more accurate device to measure temporal parameters and step length. From a practical point of view, future studies should alsoassess running power in comparison to cycling power in elite triathletes, a population with a high level in both disciplines and who could provide useful data for practical applications in training and competition

Estimation of ground reaction forces and moments during gait using only inertial motion capture

Ground reaction forces and moments (GRF&M) are important measures used as input in biomechanical analysis to estimate joint kinetics, which often are used to infer information for many musculoskeletal diseases. Their assessment is conventionally achieved using laboratory-based equipment that cannot be applied in daily life monitoring. In this study, we propose a method to predict GRF&M during walking, using exclusively kinematic information from fully-ambulatory inertial motion capture (IMC). From the equations of motion, we derive the total external forces and moments. Then, we solve the indeterminacy problem during double stance using a distribution algorithm based on a smooth transition assumption. The agreement between the IMC-predicted and reference GRF&M was categorized over normal walking speed as excellent for the vertical (ρ = 0.992, rRMSE = 5.3%), anterior (ρ = 0.965, rRMSE = 9.4%) and sagittal (ρ = 0.933, rRMSE = 12.4%) GRF&M components and as strong for the lateral (ρ = 0.862, rRMSE = 13.1%), frontal (ρ = 0.710, rRMSE = 29.6%), and transverse GRF&M (ρ = 0.826, rRMSE = 18.2%). Sensitivity analysis was performed on the effect of the cut-off frequency used in the filtering of the input kinematics, as well as the threshold velocities for the gait event detection algorithm. This study was the first to use only inertial motion capture to estimate 3D GRF&M during gait, providing comparable accuracy with optical motion capture prediction. This approach enables applications that require estimation of the kinetics during walking outside the gait laboratory

Robust foot clearance estimation based on the integration of foot-mounted IMU acceleration data

This paper introduces a method for the robust estimation of foot clearance during walking, using a single inertial measurement unit (IMU) placed on the subject's foot. The proposed solution is based on double integration and drift cancellation of foot acceleration signals. The method is insensitive to misalignment of IMU axes with respect to foot axes. Details are provided regarding calibration and signal processing procedures. Experimental validation was performed on 10 healthy subjects under three walking conditions: normal, fast and with obstacles. Foot clearance estimation results were compared to measurements from an optical motion capture system. The mean error between them is significantly less than 15 % under the various walking conditions

Implementation and validation of a stride length estimation algorithm, using a single basic inertial sensor on healthy subjects and patients suffering from Parkinson’s disease

As low cost and highly portable sensors, inertial measurements units (IMU) have become increasingly used in gait analysis, embodying an efficient alternative to motion capture systems. Meanwhile, being able to compute reliably accurate spatial gait parameters using few sensors remains a relatively complex problematic. Providing a clinical oriented solution, our study presents a gyrometer and accelerometer based algorithm for stride length estimation. Compared to most of the numerous existing works where only an averaged stride length is computed from several IMU, or where the use of the magnetometer is incompatible with everyday use, our challenge here has been to extract each individual stride length in an easy-to-use algorithm requiring only one inertial sensor attached to the subject shank. Our results were validated on healthy subjects and patients suffering from Parkinson’s disease (PD). Estimated stride lengths were compared to GAITRite© walkway system data: the mean error over all the strides was less than 6% for healthy group and 10.3% for PD group. This method provides a reliable portable solution for monitoring the instantaneous stride length and opens the way to promising applications

Inertial measurement units: a brief state of the art on gait analysis

Gait analysis systems are monitoring systems that establish a symbiosis relationship with Ambient Assisted Living (AAL) environments. Human locomotion analysis has a very important role always aiming at improving the quality of life both for individuals needing treatment or rehabilitation, as well as for healthy and elderly people. In fact, a deep and detailed knowledge about gait characteristics at a given time, and not least, monitoring and evaluating over time, will allow early diagnosis of diseases and their complications, and contribute to the decision of the treatment that should be chosen. There are several techniques used for gait measuring such as: Image Processing, Floor Sensors, and Wearable Sensors. Among the wearable sensors, has emerged an electronic device that combines multiple sensors designated by Inertial Measurement Unit (IMU). This device measures angular rate, body's specific force, and in some cases the magnetic field, and this information may be used to monitor human gait. In this article, the aim is: i) to verify the sensors that build up the IMUs, and the resulting designations that the device may have depending on the sensors it contains; ii) to list the applications of the IMUs on gait analysis; iii) to be aware of the devices available on the market and the associated commercial brands; and iv) to list the advantages and disadvantages associated with the device compared to other gait analysis systems. Concerning the literature in the scientific community, although there are some studies that focus on gait analysis or IMUs, none of them aggregates the purposes that will be addressed in this article.This work is supported by the FCT - Fundação para a Ciência e Tecnologia - with the scholarship reference SFRH/BD/108309/2015, with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) - with the reference project POCI-01-0145- FEDER-006941