Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbanging

TL;DR

This paper presents a novel approach for detecting autism-related headbanging in videos captured with moving cameras, using a head pose keypoint-based CNN-LSTM classifier that performs well with limited training data.

Contribution

It introduces a head pose keypoint feature representation and a CNN-LSTM model for activity recognition in shaky, limited-data videos, specifically targeting autism-related behaviors.

Findings

Achieved a mean F1-score of 90.77% on headbanging detection.

Demonstrated effectiveness with few training examples and unstable camera footage.

Validated the approach on the Self Stimulatory Behaviour Dataset (SSBD).

Abstract

Activity recognition computer vision algorithms can be used to detect the presence of autism-related behaviors, including what are termed "restricted and repetitive behaviors", or stimming, by diagnostic instruments. The limited data that exist in this domain are usually recorded with a handheld camera which can be shaky or even moving, posing a challenge for traditional feature representation approaches for activity detection which mistakenly capture the camera's motion as a feature. To address these issues, we first document the advantages and limitations of current feature representation techniques for activity recognition when applied to head banging detection. We then propose a feature representation consisting exclusively of head pose keypoints. We create a computer vision classifier for detecting head banging in home videos using a time-distributed convolutional neural network (CNN) in which a single CNN extracts features from each frame in the input sequence, and these extracted features are fed as input to a long short-term memory (LSTM) network. On the binary task of predicting head banging and no head banging within videos from the Self Stimulatory Behaviour Dataset (SSBD), we reach a mean F1-score of 90.77% using 3-fold cross validation (with individual fold F1-scores of 83.3%, 89.0%, and 100.0%) when ensuring that no child who appeared in the train set was in the test set for all folds. This work documents a successful technique for training a computer vision classifier which can detect human motion with few training examples and even when the camera recording the source clips is unstable. The general methods described here can be applied by designers and developers of interactive systems towards other human motion and pose classification problems used in mobile and ubiquitous interactive systems.