1. A Smart Phone-Based Pocket Fall Accident Detection, Positioning, and Rescue System
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 19, NO. 1, JANUARY 2015
Chih-Sheng Chen, Lih-Jen Kau
陳又銘

2. Outline INTRODUCTION SYSTEM OVERVIEW FEATURES SELECTION EXPERIMENTS
CONCLUSION

3. INTRODUCTION
The fall detection system can be roughly divided into two categories, namely, environmental monitoring based, and wearable sensor-based systems.
The environmental monitoring-based system can only function in a predefined environment where it is installed.
Most of the wearable sensor-based fall detection systems are made of a self-designed circuit module that should be placed and fastened around certain position.

4. SYSTEM OVERVIEW

5. SIGNAL ACQUISITION AND FEATURES SELECTION
Most of the smart devices are equipped with certain kinds of inertia detectors, e.g., the triaxial accelerometer (also known as G-Sensor)
Considering the availability, they use the triaxial accelerometer (G-Sensor) and the electronic compass as the major sensors for input signal acquisition and generation in the proposed system.

6. Triaxial Accelerometer
The outputs of the triaxial accelerometer will be sampled periodically as the input signals of the proposed system with a frequency of 150 Hz.
Use of the one-dimensional signal magnitude vector (SMV) S[n] as shown below

7. Triaxial Accelerometer
The 50 and 250 data samples just before and after the reference point will be recorded by the proposed system to form a sequence of 300 samples.

8. Triaxial Accelerometer
They also put the smart phone is placed in the pocket for eight kinds of normal activities, including run, walk, sit down, going upstairs, going downstairs, tread, jump.
They find in their experiments that the sequence of S[n] varies slowly around 1G after the appearance of the second feature, and the standard deviation σ of the last 50 data samples in the S[n] sequence is found to be smaller than 0.1 during a fall event.

9. Electronic Compass and Device Orientation
The pitch angle acquired by the electronic compass is used to assist in discriminating real fall events from normal activities.
The pitch, which indicates the angle between the Y-axis and the ground will be selected as the fourth feature of the proposed algorithm.
The fourth feature in this paper is determined by checking if the average pitch angle of the last 15 samples and smaller than the threshold,50.

10. HIGH-FREQUENCY CHARACTERISTIC ANALYSIS OF A FALL EVENT
They have defined the first four features that can be applied for the recognition of a fall accident event between normal activities.
But they find in their experiments that some of the normal activities is also possible in generating an S[n] sequence or pitch sequence similar to that of a fall accident event.
They analyze and compare the frequency components (spectrum) of the S[n] sequence under a fall accident event with that of normal activities

11. High-Pass Filtering of the S[n] Signal
To extract the high-frequency characteristic of the S[n] sequence, a high-pass filter with finite impulse response (FIR filter) is designed in this paper.
1) Stop-band gain (Astop): 80 dB. 2) Pass-band gain (Apass): 1 dB. 3) Stop-band cutoff frequency (Fstop): 40 Hz. 4) Pass-band frequency (Fpass): 50 Hz .

12. High-Pass Filtering of the S[n] Signal

13. Discrete Wavelet Transform (DWT)
They also apply the use of DWT so that the high-frequency details of a fall accident event can be easily observed.

14. Discrete Wavelet Transform (DWT)

15. SUPPORT VECTOR MACHINE
In this paper, the fifth feature, which is a two-dimensional vector, will be used in conjunction with a designed SVM to recognize if the elderly is suffering a fall accident event.
The two components of the feature vector are l1 and l2.

16. SUPPORT VECTOR MACHINE
X= (𝑙1,𝑙2) 𝑇
x1,x2,...,xn are 2 × 1 support vectors, n is the number of support vectors.
w1,w2,...,wn are the weights corresponding to individual support vectors.
b is the bias.
y indicates the result of classification.

17. SUPPORT VECTOR MACHINE
They use 63 positive feature vectors (i.e., the case of a fall accident event) and 60 negative feature vectors are used for the training of the SVM.

18. SYSTEM INTEGRATION

19. EXPERIMENTS
There are nine different kinds of activities including a fall down event, running, walking, sit down, go upstairs, go downstairs, tread, jump and wavering the smart phone will be evaluated, each with 50 tests.
The number of persons involved in the training group and that in the test group are both five.
The five subjects in the test group and that in the training group are different.

20. Performance Evaluation of the Proposed System
To investigate the performance of the proposed approach, the Sensitivity and Specificity will be used as the metric for recognition accuracy evaluation.

21. Performance Evaluation of the Proposed System

22. Performance Evaluation of the Proposed System

23. Comparisons to Existing State-of-the-Art Fall Accident Detector
The algorithm in State-of-the-Art applies the use of a single triaxial accelerometer as the input sensor and the classification algorithm is based on the Adaboost cascaded with a SVM.
In State-of-the-Art, the sensor is placed in various parts of the subject for performance investigation.

24. Computational Burden of the Proposed System
They investigate the computational as well as the power consumption burden of the proposed system by running the proposed fall accident detection application (APP) on the smart phone.
After 7 hours, they find the percentage on the power consumption of the proposed APP is around 9%.

25. HMM-Based Human Fall Detection and Prediction Method Using Tri-Axial Accelerometer
IEEE SENSORS JOURNAL, VOL. 13, NO. 5, MAY 2013
Lina Tong, Quanjun Song, Yunjian Ge, and Ming Liu

26. Outline INTRODUCTION SYSTEM OVERVIEW FEATURES SELECTION EXPERIMENTS
CONCLUSION

27. INTRODUCTION
The increasing aging population is one of the major social problems in 21st century worldwide.
To reduce the complexity of algorithm and improve fault tolerance, the accelerometer based methods without angle calculation is considered in this paper.
This study made use of HMM to describe human fall process through analyzing the ATS during fall events.

28. Information Acquisition
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29. Feature Extraction for ATS

30. HMM-Based Recognition Method
The special statistical properties of HMM has made it a tool for probability- based modeling to distinguish different features of a random signal sequence.
In this study, by means of information fusion on falling acceleration signal acquired, we are able to find out the regularity of human falls and therefore further to acquire motion features for the short time interval just before the collision occurs.

31. HMM-Based Recognition Method

32. HMM-Based Recognition Method
1. the number of invisible states: M = 3
2. the number of observation values: N = 8
3. initial state distribution: π1 = 1, πi = 0 (i = 2,…,M)
4. state transition matrix ( A): uniform distribution (General
principle)
5. emission matrix (B): uniform distribution (General principle)

33. HMM-Based Recognition Method
When λ = (M, N,π, A, B) is trained, it can describe the features of the course before collision in fall process.
Therefore, extract ATS from any motion that is under recognition, the output probability P (ATS|λ) can express the marching degree (probability) between the collision in fall process and the motion process under recognition.
It can be used to evaluate the risks to fall in a manner of higher P gets, more risk to fall.

34. HMM-Based Recognition Method

35. Fall Prediction and Detection Algorithm
The algorithm for detecting and predicting falls is based on taking the statistical sample set S to set threshold on P.
There two threshold are needed: P1, for fall prediction, and P2 is for fall detection.
First, set threshold P1, which is determined by Support Vector Machine (SVM), to make prediction in a time period (TP).
Second, by setting the threshold at the lowest P value during the state while the body has lost balance we can detect falls, which is recorded as P2.

36. Fall Prediction and Detection Algorithm
By means of normalization and statistical result of all P values, P1 = 0.334% and P2 = 12.3%were obtained.

37. EXPERIMENTS

38. CONCLUSION
The first paper speed up the efficiency of classification process, the early states are composed of simple and important features that allow a large number of negative samples to be quickly excluded from being regarded as a fall event.
With the proposed algorithm, the computational and power consumption burden of the system can be alleviated.
The second paper extract ATS from fall processes and study the acceleration variation regularity before the collision of body with lower objects in fall processes, and then built a HMM (λ) to describe it.
The risk to fall can be evaluated using the normalized output of λ to predict and detect fall processes