Abstract:
Technology advances to accelerate the quality and type of services provided for health care
and monitor. Wearable sensor systems, composed of small and light sensing nodes, have the
potential to revolutionize the health care system. An important application of wearable
sensors can be the detection of fall with its direction, particularly for elderly or otherwise
vulnerable people. In this thesis work, we implemented a direction-sensitive fall detection
system prototype using a single three-dimensional accelerometer and machine learning
algorithm which includes feature extraction and classification methods, e.g. PCA, SVM and
KNN. Four types of fall, forward, backward, left and right falls are detected. In addition to
the detection of a fall, it is also important to determine its direction, which could help locate
joint weakness or post-fall fracture and help decrease reaction time. Most wearable fall
detection algorithms are based on thresholds set by observational analysis for various fall
types. However, such algorithms do not generalize well for unseen data sets and their
applications in finding the directions of falls are not well recognized. A more appropriate
approach, as presented in this thesis, is a machine learning based algorithm SVM and KNN
were implemented for fall detection. Among the two methods, SVM provides better
performances which leads to 96.45% of accuracy using PCA, mean and standard deviation
features, exceeding the performances reported in the literature. The performances of the
developed system in real time were also evaluated and they were found satisfactory.
This work not only shows a machine learning algorithm that provides accuracy beyond the
currently available algorithms but also shows direction-sensitive and cost-effective fall
detection system using single 3D accelerometer.
