190
F. Othmen et al.
3.2 Data Preprocessing
Most of the proposed wearable fall detection relies, mainly, on the data preprocessing phase, including feature extraction and feature selection, as it plays a
critical role in defining an accurate fall detector [14]. In this sense, one of the
faced challenges for this placement is extracting relevant features that better
describe raw data and discriminate ADL events from a fall event, especially for
overlapped and similar data. Finding significant attributes that better illustrate
the raw data has always been a challenge depending on the device’s on-body
position. For instance, most on-wrist solution presented in the literature depends
mainly on accelerometer [4,11,20,34], while some others fuses it with other sensors like gyroscope [7,32,33], gyroscope and magnetometer [19], or heart rate
sensor [14].
Fig. 2. Proposed scenarios for data preprocessing.
This work implements SDL for classification approaches in a wrist-based fall
detection system with the aim of benefiting of its capacity to generate more discriminative features using sparse representation. For this purpose, we consider
two scenarios as demonstrated in Fig. 2. In scenario (A), the system will process
a time window of raw data, where we will test the effect of each sensor in the
system efficiency by adding one sensor at a time. The second scenario (B) experiments extracted features, as we will adopt the movement decomposition-based
feature extraction method used in [19]. We will only acknowledge the vertical
component of the movement and the orientation decomposition as it reached
the best results in the latter work. We denote VA, VV, and VD respectively as
Vertical Acceleration, Vertical Velocity, and Vertical Displacement. The Euler
angles present the spacial orientation features, i.e. yaw, pitch, and roll.
3.3 Dictionary Learning for Fall Detection
As being a branch of Machine Learning, the classification based on SDL involves
two main phases, namely the training and the testing phases. In the training
phase, the goal of the SDL algorithm is to map the low dimensional training
F. Othmen et al.
3.2 Data Preprocessing
Most of the proposed wearable fall detection relies, mainly, on the data preprocessing phase, including feature extraction and feature selection, as it plays a
critical role in defining an accurate fall detector [14]. In this sense, one of the
faced challenges for this placement is extracting relevant features that better
describe raw data and discriminate ADL events from a fall event, especially for
overlapped and similar data. Finding significant attributes that better illustrate
the raw data has always been a challenge depending on the device’s on-body
position. For instance, most on-wrist solution presented in the literature depends
mainly on accelerometer [4,11,20,34], while some others fuses it with other sensors like gyroscope [7,32,33], gyroscope and magnetometer [19], or heart rate
sensor [14].
Fig. 2. Proposed scenarios for data preprocessing.
This work implements SDL for classification approaches in a wrist-based fall
detection system with the aim of benefiting of its capacity to generate more discriminative features using sparse representation. For this purpose, we consider
two scenarios as demonstrated in Fig. 2. In scenario (A), the system will process
a time window of raw data, where we will test the effect of each sensor in the
system efficiency by adding one sensor at a time. The second scenario (B) experiments extracted features, as we will adopt the movement decomposition-based
feature extraction method used in [19]. We will only acknowledge the vertical
component of the movement and the orientation decomposition as it reached
the best results in the latter work. We denote VA, VV, and VD respectively as
Vertical Acceleration, Vertical Velocity, and Vertical Displacement. The Euler
angles present the spacial orientation features, i.e. yaw, pitch, and roll.
3.3 Dictionary Learning for Fall Detection
As being a branch of Machine Learning, the classification based on SDL involves
two main phases, namely the training and the testing phases. In the training
phase, the goal of the SDL algorithm is to map the low dimensional training