Curtin University develops system to improve performance of dancers
Almost every person wears an activity tracker, smart watch or GPS-enabled sports watch nowadays. These devices provide information on how much a person has moved such as distance covered, speed and heart rate.
But this information does not give an insight as to the nature or quality of the movement, neither does it aid in minimising an injury nor in training a new skill.
According to the report released by Curtin University, a PhD student from the Curtin School of Physiotherapy and Exercise Science aims to better measure the training volume and specific musculoskeletal loads in a cohort of female pre-professional ballet dancers by building an automated human activity recognition system.
Ms Danica Hendry studies the contributing factors towards pain and disability in dancers. Professional and pre-professional ballet dancers have an intense physical training regime, which can eventually lead to fatigue and overload injuries.
Recording and managing their physical workload is completely subjective. This may document hours spent in training, but do not take into account the frequency of specific movements and musculoskeletal loads that may lead to injury.
Because of this, her project became a collaboration that includes physiotherapists, biomechanists, and computer scientists from Curtin University and Edith Cowan University (ECU).
The Curtin Institute for Computation, the Curtin School of Physiotherapy and Exercise Science, the Civil and Mechanical Engineering at Curtin, and the ECU’s Western Australian Academy of Performing Arts (WAAPA) are all taking part in the project.
Since existing activity trackers cannot distinguish a jeté (jump) or an arabesque (leg lift) from a plié (bending at the knees), and do not record much when dancers train on one spot at the barre, the automated human activity recognition system had to be built.
Ms Hendry explained that sensors can be placed on the dancers but if the movement is not specifically identified, it is considered useless data. To address this, the dancers were videotaped so that it can be correlated against the sensor data.
Six sensors per dancer were used. Each sensor incorporated an accelerometer, a gyroscope and a magnetometer. The sensors were placed on the left and right shins, left and right thighs, sacrum and thoracic spine to document movement as each dancer worked through specific movements.
The continuous signals were then segmented and manually cross-referenced against the video footage in order to connect specific signal segments to individual dance movements.
Because each dancer is different, the research team had to record 23 dancers from WAAPA as they worked through a sequence of dance movements. The study focused on jumps as the force exerted on the body during landing are implicated for lower limb injury. Leg lifts were also observed as they are implicated for hip and lower back pain.
In order to make sense of large data sets, Ms Hendry turned to machine learning. CIC specialist Dr Kevin Chai led the team that built a convolutional neural network. The network was trained by using Ms Hendry’s library of manually-classified movement data.
Training let the network identify patterns and diagnostic features in the mass of sensor data that had been correlated with different jumps and leg lifts through the video.
Using data gathered from all six sensors, the network could identify target movements with 80% accuracy, which was enough to assess training load.
During the process, the team learned that with data coming from only one sensor, the one placed on the sacrum, the neural network still had over 75% accuracy. Having only one sensor, which can be hidden under a costume, opens up avenues to studying performance, not just training.
52 dancers are now being recorded over an entire day of training, four times across a semester. The trained neural network is then used to convert the data into a quantitative measure of jumping and leg-lifting training volume for each.
During each data collection day, the dancers complete a survey that assesses a range of emotional, cognitive and lifestyle factors, pain experienced, and limitations faced during training.
The data will then be used to look at the trajectory of each dancer across the semester and explore the various factors that correlate with pain and disability.
WAAPA Biomechanist Dr Luke Hopper shared that they want to assist the pre-professional dancers to reach the challenging heights of being professional dancers. A tool like this can be calibrated to focus on training, and measure outputs comparable to live performances.
Curtin Biomechanist and Ms Hendry’s supervisor, Dr Amity Campbell explained that field-based analysis is the new way of doing biomechanical research. The dancers can be captured in their normal environment
Bringing them, she added, in laboratories with cameras and sensors will not capture normal performance pressures. Capturing their activity in real conditions will be so much more useful for injury prevention, performance development, and high-performance training.
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