A Novel On-Wrist Fall Detection System Using SDL Technique
185
rate [17]. Falling is one of the most crucial health risks faced by this fragile
population, classified as a disease in the International Classification of Diseases
[27]. According to [16], the risk of falling varies from 30% for elderly over 65 to
50% for those over 85 each year.
Wearable fall detection systems have captivated much interest in later years
literature as they can fit easily into smart wearable accessories like wristbands assuring anywhere-anytime accessibility and comfortable use compared
to other existing solutions, i.e the vision and ambient-based [22]. Commonly,
state-of-the-art methods for wearable fall detectors are either threshold-based or
machine learning-based, for which the latter received superior interest recently
[29]. Abstracting an optimal combination between extracted features and classifiers, while enhancing system reliability, has been extensively researched in most
related works [19,23]. However, classification performance can degrade substantially, as hand-crafted features may be very specific to the sensor, device placement, or dataset [2,8].
Dictionary learning approaches (DLA) have gained a lot of enthusiasm in
image processing including sparse representation based classification algorithm
for face recognition [30], as it has shown robustness especially for a limited number of channels and samples, thus reducing the need to select the best feature
combination and classifier for the application. Therefore, DLA has been recently
emerged into the biomedical signal processing field, of which some associated
works have been proposed mainly for Electroencephalography (EEG) and electrocardiogram (ECG) signal classification [3,13].
In the same direction, we propose in this paper a novel on-wrist fall detection system based on Supervised Dictionary Learning (SDL), to autonomously
generate optimal features selection that best represents acquired data. Indeed,
the work presented here extends previous study [19] that implemented a movement decomposition method to extract features (direction components and body
orientation) and machine learning algorithms for fall detection based on wrist
wearable device. For evaluation purposes, three SDL and sparse representations
algorithms with different experimental situations will be assessed throughout this
paper, besides comparing it with previous related works. In this context, multiple sensors and features combinations in different experimental arrangements
will be used. To the extent of our knowledge, such a dictionary-based approach
is still underexplored in the related literature, so it is the main contribution of
this work.
The remainder of this paper is organized as follows. Section 2 presents the
main theoretical background behind our study. A detailed description of our
proposed method is provided in Sect. 3. Section 4 illustrates the obtained results
and compares them with prior works. Conclusion and future related work are
provided in Sect. 5.
185
rate [17]. Falling is one of the most crucial health risks faced by this fragile
population, classified as a disease in the International Classification of Diseases
[27]. According to [16], the risk of falling varies from 30% for elderly over 65 to
50% for those over 85 each year.
Wearable fall detection systems have captivated much interest in later years
literature as they can fit easily into smart wearable accessories like wristbands assuring anywhere-anytime accessibility and comfortable use compared
to other existing solutions, i.e the vision and ambient-based [22]. Commonly,
state-of-the-art methods for wearable fall detectors are either threshold-based or
machine learning-based, for which the latter received superior interest recently
[29]. Abstracting an optimal combination between extracted features and classifiers, while enhancing system reliability, has been extensively researched in most
related works [19,23]. However, classification performance can degrade substantially, as hand-crafted features may be very specific to the sensor, device placement, or dataset [2,8].
Dictionary learning approaches (DLA) have gained a lot of enthusiasm in
image processing including sparse representation based classification algorithm
for face recognition [30], as it has shown robustness especially for a limited number of channels and samples, thus reducing the need to select the best feature
combination and classifier for the application. Therefore, DLA has been recently
emerged into the biomedical signal processing field, of which some associated
works have been proposed mainly for Electroencephalography (EEG) and electrocardiogram (ECG) signal classification [3,13].
In the same direction, we propose in this paper a novel on-wrist fall detection system based on Supervised Dictionary Learning (SDL), to autonomously
generate optimal features selection that best represents acquired data. Indeed,
the work presented here extends previous study [19] that implemented a movement decomposition method to extract features (direction components and body
orientation) and machine learning algorithms for fall detection based on wrist
wearable device. For evaluation purposes, three SDL and sparse representations
algorithms with different experimental situations will be assessed throughout this
paper, besides comparing it with previous related works. In this context, multiple sensors and features combinations in different experimental arrangements
will be used. To the extent of our knowledge, such a dictionary-based approach
is still underexplored in the related literature, so it is the main contribution of
this work.
The remainder of this paper is organized as follows. Section 2 presents the
main theoretical background behind our study. A detailed description of our
proposed method is provided in Sect. 3. Section 4 illustrates the obtained results
and compares them with prior works. Conclusion and future related work are
provided in Sect. 5.