# Design of a Wearable Healthcare Emergency Detection Device for Elder Persons
## 4 Results  
How to evaluate fall detection systems (FDSs) in realistic conditions is still an unresolved experimental problem. The main users of FDSs are supposed to be the elderly. The current public databases containing actual falls experienced by older adults are certainly scarce. In our scenario of monitoring older persons' activities (generally people with limited mobility), falls could be identified as movements that clearly deviate from the detected patterns of the samples in the training set. Anyway, in the absence of measurement repositories with a significant number of actual falls, experiments were conducted to obtain the acceleration values of falls. We excluded older people from falls simulations because they could have resulted in severe injuries to such subjects.  
For fall simulations, we prepared an experimental environment consisting of a floor mat capable of absorbing one's fall on which we put an unstable platform. The subject, to which the smart device was attached on the right wrist by a strip string, was asked to stand on the unstable platform. By slightly moving the platform, the subject's fall was induced.  
The model for motion detection (ADL, fall, walk, run) was trained for 80 epochs, obtaining the results shown in Figure 3.  
The parameters shown are:  
* _loss_, defined as the root mean square error between the actual value and the predicted value during training;
* _accuracy_, as the percentage of correct predictions, compared to the total predictions during training;
* _val_loss_, loss on the validation data;
* _val_accuracy_, accuracy on the validation data.  
Figure 2(a) shows the loss and _val_loss_ obtained for the motion classifier. On the validation data, the loss function was 0.10. Figure 2(b) shows the accuracy and _val_accuracy_ obtained for the motion classifier. On the validation data, the accuracy did not get beyond 78%.  
For a real-world validation session, we tested an elderly person performing ADLs and walking, since they are safe experiments. These data are more representative of the posture, walking speed and other factors typical of older people.  
The subject performed ADLs wearing the device for a week and provided feedback on the alert notifications prompted by the smartphone. The subject also performed a prefixed set of activities at the end of each day to compare the results. We estimated network classification performance at the beginning and the end of the experiment by evaluating samples of data collected during test set of activities performed on day 1 and day 7. We reported the results in Figure 4.  
Figure 3: Training of the movement detection model: (**a**) _loss_ and _val_loss_ functions for the movement classifier. (**b**) _accuracy_ and _val_accuracy_ functions for the movement classifier.  
Figure 4a shows the confusion matrix obtained by the model during day 1. The recognition of fall, walk and run activities were good (all five running activities were recognised, along with the four walking activities and the two falling activities). However, in the case of the ADLs, due to their variability, only one was recognised adequately, and the remainder were predicted to be falls. Figure 4b shows the model's confusion matrix obtained during day 7, after tuning the network with feedback provided by the subject through the smartphone. Fall, walk and run recognition were still good; and in ADLs recognition, 50% of events were correctly recognised.