Recognition of Daily Gestures with Wearable Inertial Rings and Bracelets

Abstract

Recognition of activities of daily living plays an important role in monitoring elderly people and helping caregivers in controlling and detecting changes in daily behaviors. Thanks to the miniaturization and low cost of Microelectromechanical systems (MEMs), in particular of Inertial Measurement Units, in recent years body-worn activity recognition has gained popularity. In this context, the proposed work aims to recognize nine different gestures involved in daily activities using hand and wrist wearable sensors. Additionally, the analysis was carried out also considering different combinations of wearable sensors, in order to find the best combination in terms of unobtrusiveness and recognition accuracy. In order to achieve the proposed goals, an extensive experimentation was performed in a realistic environment. Twenty users were asked to perform the selected gestures and then the data were off-line analyzed to extract significant features. In order to corroborate the analysis, the classification problem was treated using two different and commonly used supervised machine learning techniques, namely Decision Tree and Support Vector Machine, analyzing both personal model and Leave-One-Subject-Out cross validation. The results obtained from this analysis show that the proposed system is able to recognize the proposed gestures with an accuracy of 89.01% in the Leave-One-Subject-Out cross validation and are therefore promising for further investigation in real life scenarios.

Keywords: activities of daily living; gesture recognition; machine learning; sensor fusion; wearable sensors.

Figure 1

Placement of inertial sensors on the dominant hand and on the wrist. In the circle a focus on the placement of the SensHand is represented, while in the half-body figure the position of the wrist sensor is shown.

Figure 2

Focus on grasping of objects involved in the different gestures (a) grasp some chips with the hand (HA); (b) take the cup (CP); (c) grasp the phone (PH); (d) take the toothbrush (TB).

Figure 3

Example of (a) eating with the hand gesture (HA); (b) drink from the cup (CP); (c) answer the telephone (PH); (d) brushing the teeth (TB).

Figure 4

Precision, recall and specificity of personal analysis for (a) DT and (b) SVM.

Figure 5

Precision, recall and specificity of impersonal analysis for (a) DT and (b) SVM.

Figure 6

Precision, recall and specificity of SVM impersonal analysis for (a) Hand gesture; (b) Glass gesture; (c) Fork gesture; (d) Spoon gesture; (e) Cup gesture; (f) Phone gesture; (g) Toothbrush gesture; (h) Hairbrush gesture; (i) Hair dryer gesture.

Figure 6

Precision, recall and specificity of SVM impersonal analysis for (a) Hand gesture; (b) Glass gesture; (c) Fork gesture; (d) Spoon gesture; (e) Cup gesture; (f) Phone gesture; (g) Toothbrush gesture; (h) Hairbrush gesture; (i) Hair dryer gesture.