# Design of a Wearable Healthcare Emergency Detection Device for Elder Persons
## 5 Conclusions  
In this work we have presented a system for fall detection for elderly people. The system exploits a smart sensor board on which we use a neural network trained to recognise and monitor the activity of the patient. The board interacts with a smartphone application, connected through Bluetooth with the board, which is responsible for getting user feedback to supposed fall events and forwarding emergency calls if necessary.  
In previous fall detection studies, falls have recognised using acceleration sensors on the waist or the chest, and the recognition rate has been over 95%. However, when an acceleration sensor on the wrist was used, the recognition rate was about 75%. The artificial neural network proposed in this work was able to recognise activities with 78% accuracy using the acceleration of the wrist. This is a relatively small improvement compared to the conventional fall detection mechanism, which is due to the simple neural network model that was designed to suit the limited computational capabilities of such devices.  
However, with wrist-band type devices, we can cut down the system costs (we may use existing smart-watches or bands) and provide comfort to the user. Moreover the proposed system is portable, usable in outdoor environments and upgradeable through the firmware. Furthermore, the system analyses sensor data with an embedded computational unit (CU), not having the need for streaming data to an external CU, thereby preventing draining of the battery of the connected smartphone. The latter is only responsible for obtaining feedback from the user and forwarding emergency notifications.  
In future developments we will provide the ability to classify more activities, so that the living patterns of older persons can be better recognised. Furthermore, we could also integrate speech recognition features to recognise help requests, including those not related to falls [19; 20]. Moreover, we foresee the need to apply security and privacy techniques in order to process the data acquired by the sensors [21; 22].  
Figure 4: Model’s confusion matrix. (**a**) Confusion matrix for the movement detection classifier on 1. (**b**) Confusion matrix for the movement detection classifier on day 7.  
**Author Contributions:** Conceptualization, F.A., W.B. and G.C.; methodology, F.A., W.B. and G.C.; software, F.A., W.B. and G.C.; validation, F.A., W.B. and G.C.; formal analysis, F.A., W.B. and G.C.; data curation, F.A., W.B. and G.C.; writing--original draft preparation, F.A., W.B. and G.C.; writing--review and editing, F.A., W.B. and G.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** [[https://sites.google.com/up.edu.mx/har-up/](https://sites.google.com/up.edu.mx/har-up/)]([https://sites.google.com/up.edu.mx/har-up/](https://sites.google.com/up.edu.mx/har-up/)), [[https://www.kaggle.com/vmalyi/run-or-walk](https://www.kaggle.com/vmalyi/run-or-walk)]([https://www.kaggle.com/vmalyi/run-or-walk](https://www.kaggle.com/vmalyi/run-or-walk)) (accessed on 18 November 2021).  
**Conflicts of Interest:** The authors declare no conflict of interest.