Hello everyone, my name is Jie Lian.
A phd student from University of Louisiana at Lafayette.
Today I will present our work,
Fall Detection via Inaudible Acoustic Sensing.
Falls are common among people.
In each year, more than 37 million people require medical attention due to a fall.
Among them, about 3 million older people would be treated in hospitals due to falls.
Falls are dangerous.
Injuries caused by fall could lead to death.
For elders could not standing up independently after the fall, the fall is more dangerous.
Because lying on the floor for a long time after the fall may aggravate the injury.
As such a fall detection system is important to protect the elders.
Recent year the fall detection has attract a lot of research interest.
There are mainly two types of fall detection system.
Wearable based fall detection system and mon-wearable based fall detection system.
The wearable based system could be accurate but need to wear it all the time to monitor the activity.
Unfortunately the elders may forget to wear such a device.
The popular non-wearable based fall detection system are based on Wi-fi, radar, or camera.
Wi-fi based system would affect the communication channel and also require additional hardware to process the CSI signal.
The radar-based system need dedicate hardware, incur additional cost.
The camera based system may invade the privacy.
To tackle the problems of the current fall detection system, we propose an ultrasound based fall detection system.
The key idea of our approach is to detect the fall by leveraging the ultrasonic Doppler signal of the fall.
As the above figure shows, the fall has a unique Doppler signal.
So, we design a system to distinguish the Doppler signal of fall from other activities.
Here is the overview of our system.
There are six module in our system.
In the Sensing module,
we let a speaker generates a 20khz continuous wave.
Then let a microphone record the Doppler signal of the activities.
Then in the Signal Processing module, we denoise the signal.
We apply the STFT to get the spectrogram of the signal
We apply the spectral subtraction and a threshold to cancel the noise.
As you can see, after the signal processing module, we get a clean spectrogram of the fall.
In the motion locating module,
we apply the power burst curve on the clean spectrogram to locate the motion.
And we estimate the duration time of the motion.
So we only need to process the signal contain the motion, instead of processing the whole spectrogram.
In the Feature Extraction module, we extract three set of features from the spectrogram.
The first set of features are torso, leg, arm speed curve, which represent the body movement speed.
Usually each part of the body would have a relatively high speed during the fall compare to other activities.
The second set of feature is extreme frequency ratio curve, usually the fall would have a sharper curve.
The third set of feature is spectral entropy, represent the randomness of the energy distribution on spectrogram.
Considering the quick and sudden motions of a fall event, it usually has a higher entropy than other events.
Before the classification, we need to reduce the feature dimension and determine the number of the hidden state.
We apply singular value decomposition to reduce the dimension of the features.
And apply the K-means clustering to determine the number of hidden states in the Hidden Markov Model.
Finally we could train a Hidden Markov Model by the prerecorded fall data.
The Hidden Markov Model would outputs the probability that the input data is fall.
There are 23 volunteers in our experiment.
Among them there are 6 elders and 17 young people.
The age of the volunteers ranging from 24 to 75.
We use a speaker generate 20khz ultrasound .
And a microphone to record the Doppler signal
We conduct experiments in four different environments.
In each environment, we ask the volunteers to perform the fall activities, fall like activities and daily activities.
Then we record the Doppler signal of different activities.
This experiment is to show system’s performance in different environments.
In this experiment we train a Hidden Markov Model with the data collected from two participants in the apartment,
and then examine it with different people in different environment.
In this experiment, the participants perform a series of activities including Fall, Fall-like and Daily activities in each environment.
As figure shows, each metric is above 90%, showing our system is robust to different environment and person.
This experiments is to show system’s performance in detecting different types of non-fall activities.
Three types of non-fall activities are evaluated such as objects falling from high place, fall-like activities, and some daily activities.
All metrics greater than 90%, showing our system could distinguish the non-fall activities effectively.
In conclusion,
in this talk, we present an ultrasonic based fall detection system.
We conduct extensive experiment to show the performance and robustness of our system.
Thank you for listening.