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. 2022 Oct 28;22(21):8273.
doi: 10.3390/s22218273.

Classification of Activities of Daily Living Based on Grasp Dynamics Obtained from a Leap Motion Controller

Hajar Sharif  1 , Ahmadreza Eslaminia  1 , Pramod Chembrammel  1 , Thenkurussi Kesavadas  1
Affiliations

Classification of Activities of Daily Living Based on Grasp Dynamics Obtained from a Leap Motion Controller

Hajar Sharif et al. Sensors (Basel). .

Abstract

Stroke is one of the leading causes of mortality and disability worldwide. Several evaluation methods have been used to assess the effects of stroke on the performance of activities of daily living (ADL). However, these methods are qualitative. A first step toward developing a quantitative evaluation method is to classify different ADL tasks based on the hand grasp. In this paper, a dataset is presented that includes data collected by a leap motion controller on the hand grasps of healthy adults performing eight common ADL tasks. Then, a set of features with time and frequency domains is combined with two well-known classifiers, i.e., the support vector machine and convolutional neural network, to classify the tasks, and a classification accuracy of over 99% is achieved.

Keywords: Leap Motion Controller; activities of daily living; hand grasps classification.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 2
Figure 2
Leap Motion Controller connected to a computer that runs the Leap Motion Visualizer software showing the hands on top of the LMC camera [51].
Figure 3
Figure 3
Experimental setup (a) and hand model in the global coordinate system (b).
Figure 4
Figure 4
LMC’s interaction zone [53].
Figure 1
Figure 1
Stroke timeline [3].
Figure 5
Figure 5
ADL tasks [54].
Figure 6
Figure 6
Hand joints and palm center [55].
Figure 7
Figure 7
Hand coordinate system [56].
Figure 8
Figure 8
Leap Motion Controller frame of reference [57].
Figure 9
Figure 9
Proposed CNN architecture [54].
Figure 10
Figure 10
Confusion matrices for sample combinations of features and classifiers. All values were obtained through 5-fold cross validation and are presented as percentages (%).
See this image and copyright information in PMC

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