Fall Recognition Based on an IMU Wearable Device and Fall Verification through a Smart Speaker and the IoT

Abstract

A fall is one of the most devastating events that aging people can experience. Fall-related physical injuries, hospital admission, or even mortality among the elderly are all critical health issues. As the population continues to age worldwide, there is an imperative need to develop fall detection systems. We propose a system for the recognition and verification of falls based on a chest-worn wearable device, which can be used for elderly health institutions or home care. The wearable device utilizes a built-in three-axis accelerometer and gyroscope in the nine-axis inertial sensor to determine the user's postures, such as standing, sitting, and lying down. The resultant force was obtained by calculation with three-axis acceleration. Integration of three-axis acceleration and a three-axis gyroscope can obtain a pitch angle through the gradient descent algorithm. The height value was converted from a barometer. Integration of the pitch angle with the height value can determine the behavior state including sitting down, standing up, walking, lying down, and falling. In our study, we can clearly determine the direction of the fall. Acceleration changes during the fall can determine the force of the impact. Furthermore, with the IoT (Internet of Things) and smart speakers, we can verify whether the user has fallen by asking from smart speakers. In this study, posture determination is operated directly on the wearable device through the state machine. The ability to recognize and report a fall event in real-time can help to lessen the response time of a caregiver. The family members or care provider monitor, in real-time, the user's current posture via a mobile device app or internet webpage. All collected data supports subsequent medical evaluation and further intervention.

Keywords: Internet of Things; fall recognition; fall verification; smart speaker.

Figure 3

Falling verification and the IoT care structure.

Figure 6

(A) The software flow diagram of the system while the FSM is executed. (B) The parameter of eight kinds of behavior states.

Figure 6

(A) The software flow diagram of the system while the FSM is executed. (B) The parameter of eight kinds of behavior states.

Figure 1

(A) The circuit board layout of the chest-worn wearable device. (B) The appearance of the chest-worn wearable device.

Figure 2

Real-time monitoring and data collection in the iOS tablet app.

Figure 4

Active push broadcast structure.

Figure 5

Behavior state switching using the FSM.

Figure 7

The change in the resultant force and pitch angle of falling backward while sitting on a chair.

Figure 8

The change in the atmospheric pressure and state of falling forward while sitting on a chair.

Figure 9

The change in the resultant force and roll angle of falling to the right while sitting on a chair.

Figure 10

The change in the atmospheric pressure and state of falling to the left while sitting on a chair.

Figure 11

The change in the resultant force and pitch angle of falling forward while standing.

Figure 12

The change in the atmospheric pressure and state of falling backward while standing.

Figure 13

The change in the resultant force and roll angle of falling to the right while standing.

Figure 14

The change in atmospheric pressure and state of falling to the left while standing.

Figure 15

The change in the resultant force and pitch angle of falling backward while standing up.

Figure 16

The change in the atmospheric pressure and state of falling forward while standing up.

Figure 17

The change in the resultant force and roll angle of falling to the right while standing up.

Figure 18

The change in the atmospheric pressure and state of falling to the left while standing up.

Figure 19

Fall push casting process.

Figure 20

Physiological signal pushing process after a fall event.