In the posture analysis, a distance map of two sampling human skeletons is calculated every 0.4 second, and a human fall is detected if this distance map is larger than a threshold, i.e., only a drastic change in the human posture as discussed in Section II.1 is accepted as showing human falling. On the other hand, the shape analysis only uses the change rate in the ellipse angle and the ratio between the major and minor semi-axes of the ellipse to detect human falling, i.e., only a drastic change in the human shape as discussed in Section II.2 is accepted as indicating human falling.
Tables 4 and 5 show experimental results for the human skeleton match and the posture analysis respectively. The experimental results for shape analysis utilizing, the change ratio of two ellipse features, are tabulated in Table 6. From Tables 4 to 6, we can observe that the fall detection systems based on a single approach yield a high false positive rate and low detection accuracy because they cannot differentiate a sit-down from a fall-down.
Table 4 The experimental results of the skeleton match
Event No of videos
Detected falling
Detected non-falling
TP TN FP FN
Falling 22 16 6 16 0 0 6 Sit-down 8 1 7 0 7 1 0 Squat 8 0 8 0 8 0 0 Walking 8 0 8 0 8 0 0 Running 8 0 8 0 8 0 0
Table 5 The experimental results of the posture analysis
Table 6 The experimental results of the shape analysis
Event No of
Table 7 The experimental results of the proposed fall detection with the skeleton distance = 0.056
Event No of
Table 8 The comparisons of four fall detection schemes
Fall detection approach Detection rate False alarm rate The skeleton match 0.727 0.031 The posture analysis 1 0.34
The shape analysis 0.909 0.16 The proposed scheme 0.909 0.06
Table 9 Comparison of the proposed scheme and the shape analysis in terms of the execution time, detection rate and false alarm rate
Performance metric The proposed detection scheme
The shape analysis Detection rate 90.9% 90.9 % False alarm rate 6.25 % 15.6 % Execution Time 4.21 sec 1.36 sec
The experimental results for the proposed falling detection system are shown in Table 7. Two fall-down incidents were not detected because they were slow-speed fall incidents, which did not register as fast posture changes in the first step of the proposed approach. On the other hand, two sit-down activities were flagged fall incidents because fast sit-down activities trigger a posture change step in the proposed approach. Table 8 summarizes the performance of the four human fall detection approaches in terms of the detection rate and the false alarm rate. These results demonstrate that an intelligent combination of different fall detection approaches can provide reliable fall detection. Table 9 compares the proposed hybrid human detection approach with the shape analysis approach (Rougier et al, 2007) in terms of the
execution time, the detection rate and the false alarm rate. We can observe from Tables 8 and 9 that our approach can achieve high detection accuracy and a lower false alarm rate than other systems within a reasonable execution time.
CONCLUSIONS
Since the global population is aging rapidly, fall detection for aging people has become an important issue in smart homes. The major contribution of this paper is to propose a novel real-time fall detection approach for elderly people, which is an intelligent combination of the skeleton change and the shape change detection scheme while still satisfying the real-time constraint. A human skeleton is first extracted from a human posture. The distance between two sampling skeletons beyond a threshold flags a posture change. We then use an ellipse to approximate the human shape. The orientation and the ratio of the major and the minor semi-axes of the ellipse are used to detect human shape change. Finally, we confirm a human fall incident by monitoring the inactivity of a person for a period of time. Experimental results indicate that the proposed hybrid human fall detection system can achieve a high detection rate and a low false alarm rate with reasonable computational costs.
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