James Bruce Lee

The use of a single inertial sensor to identify stride, step, and stance durations of running gait.

Current developments in inertial sensor technology could enable the measurement of running gait outside of the traditional laboratory environment. The purpose of this research was to determine the level of agreement between an inertial sensor and infrared camera based estimates of stride, step, and stance durations across a range of running speeds. An inertial sensor was placed on the sacrum of 10 elite national standard runners, and the stride, step, and stance of running gait were compared. A total of 504 samples were collected and the running velocities stratified into three equal groups of low (10-12 km/h), medium (13-15 km/h), and high (16-19 km/h). A single inertial sensor was found to be suitable for identifying stride duration with Bland-Altman limits of agreement of 95%. The stride data showed agreement at less than 0.02s for most limits. Agreement for step showed five of the eight upper and lower limits below 0.02s. The largest differences between both capture methods were for stance. An average bias of 0.0008s was found and standard error ranged between 0.0004s and 0.0009s across all variables. The results from this research found that inertial sensors are suitable to measure stride, step, and stance duration, and provide the opportunity to measure running gait outside of the traditional laboratory.

Identifying symmetry in running gait using a single inertial sensor

Running gait has been shown to alter due to changes in intensity. It was hypothesised that a sacral mounted single inertial sensor could identify the centre of mass (COM) vertical accelerations. This study aimed to validate this new technique against a criterion measure and to determine the influence of changes in running intensity on COM vertical acceleration and the symmetry of COM vertical acceleration between left and right steps. Ten athletes ran for 5min at their self-selected pace, plus 1km/h above and below this velocity. Validity of the single inertial sensor was determined by comparing COM vertical acceleration against that measured with a six-camera infrared system. Large correlation (r=0.96), a small typical error of estimate (1.84), and mean bias (0.02) were found between the two systems. The greatest magnitude in COM vertical acceleration occurred at the slowest running pace and decreased as pace increased. Sixty percent of the athletes exhibited asymmetry during at least one running pace; 30% were asymmetrical across all three velocities. While significant changes in COM vertical acceleration occurred between the different running velocities, this did not always result in a change in symmetry. This study found that a single inertial sensor can be used as a valid means of measuring COM vertical acceleration. This technique can detect changes in the COM vertical acceleration that may change with running velocity. Gait symmetry (using COM vertical acceleration) during running was also quantified using the inertial sensor.

Detection of Illegal Race Walking: A Tool to Assist Coaching and Judging

Current judging of race walking in international competitions relies on subjective human observation to detect illegal gait, which naturally has inherent problems. Incorrect judging decisions may devastate an athlete and possibly discredit the international governing body. The aim of this study was to determine whether an inertial sensor could improve accuracy, monitor every step the athlete makes in training and/or competition. Seven nationally competitive race walkers performed a series of legal, illegal and self-selected pace races. During testing, athletes wore a single inertial sensor (100 Hz) placed at S1 of the vertebra and were simultaneously filmed using a high-speed camera (125 Hz). Of the 80 steps analyzed the high-speed camera identified 57 as illegal, the inertial sensor misidentified four of these measures (all four missed illegal steps had 0.008 s of loss of ground contact) which is considerably less than the best possible human observation of 0.06 s. Inertial sensor comparison to the camera found the typical error of estimate was 0.02 s (95% confidence limits 0.01–0.02), with a bias of 0.02 (±0.01). An inertial sensor can thus objectively improve the accuracy in detecting illegal steps (loss of ground contact) and, along with the ability to monitor every step of the athlete, could be a valuable tool to assist judges during race walk events.

Validating Temporal Motion Kinematics from Clothing Attached Inertial Sensors

A major barrier to wearables utility for kinematic analysis is convenience. Attachment to skin requires significant expertise and is time consuming. Instead this research applies principles of sensor analysis to clothing attachments, to examine temporal immunity to clothing artefact. No known research has validated temporal outputs of inertial measurement units when embedded in clothing. Nine participants completed five repetitions of conventional deadlifts while being monitored with inertial sensors fixed on anatomical landmarks and embedded in clothing. The agreement of group means between timing outputs of inertial sensor anteroposterior axis data were compared between sensor locations. Will Hopkins Typical Error of the Estimate, Pearsons correlation and a Bland Altman Limits of Agreement analysis were implemented for validation. Strong agreement was found based on trivial standardised error (<0.1) for all agreement analyses. Results support past research for applications applying temporal features of wearables to monitor human movement.

An Architectural Based Framework for the Distributed Collection, Analysis and Query from Inhomogeneous Time Series Data Sets and Wearables for Biofeedback Applications

The increasing professionalism of sports persons and desire of consumers to imitate this has led to an increased metrification of sport. This has been driven in no small part by the widespread availability of comparatively cheap assessment technologies and, more recently, wearable technologies. Historically, whilst these have produced large data sets, often only the most rudimentary analysis has taken place (Wisbey et al in: “Quantifying movement demands of AFL football using GPS tracking”). This paucity of analysis is due in no small part to the challenges of analysing large sets of data that are often from disparate data sources to glean useful key performance indicators, which has been a largely a labour intensive process. This paper presents a framework that can be cloud based for the gathering, storing and algorithmic interpretation of large and inhomogeneous time series data sets. The framework is architecture based and technology agnostic in the data sources it can gather, and presents a model for multi set analysis for inter- and intra- devices and individual subject matter. A sample implementation demonstrates the utility of the framework for sports performance data collected from distributed inertial sensors in the sport of swimming.