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F. Othmen et al.
2 Theoretical Background and Related Work
2.1 Wearable Fall Detection System
Wearable-based fall detection systems illustrate all on-body attached garment
devices that usually embed inertial measurement units (IMU) to inspect the
body’s motions, positions, and rotation movements in the space [22]. Commonly,
inertial sensors such as accelerometers, gyroscopes, and magnetometers are the
most used for fall detection to discriminate and notify the occurrence of a fall
event as soon as possible [15]. It mostly presents an ideal solution for indoor
and outdoor monitoring, especially with the emergence of nowadays advances in
wearable technologies like a pendant, band, and glasses to make it more comfortable and tolerable to be wear.
Most of the analysis methods being employed in wearable fall detection are
grounded on threshold and machine learning algorithms [10]. Threshold-based
approaches usually compare the sensor’s acquired data (or extracted features)
with a predefined threshold(s) and a fall is detected when the predefined value
is exceeded [26]. However, these algorithms are practically unreliable as fall is
often confused with other activities like jumping. Additionally, a huge amount of
soft falls are likely to be unidentified, due to their low threshold [6]. To enhance
the accuracy limitation of the threshold algorithms, the literature proposed various machine learning-based solutions through classification algorithms like SVM,
ANN, KNN, etc [1,23]. These algorithms are more efficient as they can globe a
greater number of fall types, yet very dependent of the on-body placement. Thus,
machine-learning algorithms have shown impressive practical results when placed
in steady body location (near gravity point of the body) such as waist and chestworn. Otherwise, they are less efficient especially when placed in extremities such
as wrist, requiring further investigations to improve the performance in those
cases, mainly because wrist-based solutions are the most comfortable from a user
point of view and less associated to the stigma of using a medical device [12,18,19].
2.2 Dictionary Learning for Classification
Sparse Representation and Supervised Dictionary Learning Characteristics. DLA has received a lot of interest as a representation learning
paradigm by achieving state-of-the-art performance in many practical fields in
computer vision such as information retrieval, image restoration, and classification [5].
It has been observed that DLA intends to learn a dictionary directly from
the training samples by generating the space where the given signal could be
represented properly to provide improved processing and better results in fitted
to the problem domain. In DLA models, given a set X = [x 1 ,... , x m ] of m
samples, the objective is to generate a dictionary D which maps a high and
sparse dimensional representation denoted A for each input sample. Generally,
F. Othmen et al.
2 Theoretical Background and Related Work
2.1 Wearable Fall Detection System
Wearable-based fall detection systems illustrate all on-body attached garment
devices that usually embed inertial measurement units (IMU) to inspect the
body’s motions, positions, and rotation movements in the space [22]. Commonly,
inertial sensors such as accelerometers, gyroscopes, and magnetometers are the
most used for fall detection to discriminate and notify the occurrence of a fall
event as soon as possible [15]. It mostly presents an ideal solution for indoor
and outdoor monitoring, especially with the emergence of nowadays advances in
wearable technologies like a pendant, band, and glasses to make it more comfortable and tolerable to be wear.
Most of the analysis methods being employed in wearable fall detection are
grounded on threshold and machine learning algorithms [10]. Threshold-based
approaches usually compare the sensor’s acquired data (or extracted features)
with a predefined threshold(s) and a fall is detected when the predefined value
is exceeded [26]. However, these algorithms are practically unreliable as fall is
often confused with other activities like jumping. Additionally, a huge amount of
soft falls are likely to be unidentified, due to their low threshold [6]. To enhance
the accuracy limitation of the threshold algorithms, the literature proposed various machine learning-based solutions through classification algorithms like SVM,
ANN, KNN, etc [1,23]. These algorithms are more efficient as they can globe a
greater number of fall types, yet very dependent of the on-body placement. Thus,
machine-learning algorithms have shown impressive practical results when placed
in steady body location (near gravity point of the body) such as waist and chestworn. Otherwise, they are less efficient especially when placed in extremities such
as wrist, requiring further investigations to improve the performance in those
cases, mainly because wrist-based solutions are the most comfortable from a user
point of view and less associated to the stigma of using a medical device [12,18,19].
2.2 Dictionary Learning for Classification
Sparse Representation and Supervised Dictionary Learning Characteristics. DLA has received a lot of interest as a representation learning
paradigm by achieving state-of-the-art performance in many practical fields in
computer vision such as information retrieval, image restoration, and classification [5].
It has been observed that DLA intends to learn a dictionary directly from
the training samples by generating the space where the given signal could be
represented properly to provide improved processing and better results in fitted
to the problem domain. In DLA models, given a set X = [x 1 ,... , x m ] of m
samples, the objective is to generate a dictionary D which maps a high and
sparse dimensional representation denoted A for each input sample. Generally,