# Design of a Wearable Healthcare Emergency Detection Device for Elder Persons
## 2 Related Works  
At present, several solutions have been proposed for elderly fall detection. Such solutions are categorised into three main types according to the sensor-technology used: non-wearable systems (NWS), wearable systems (WS) and fusion or hybrid systems (FS).  
In particular, NWS systems [3; 4; 5] use vision-based sensors strategically distributed in the home of the elder. They have been proven powerful and robust at detecting falls; however, these systems have high costs, can be obviously be effective only in indoor environments and could generate privacy issues for the elders or the people that assist them.  
To overcome these limitations, WS systems were proposed. They typically use inertial sensors, such as an accelerometer or gyroscope, usually attached to the elder for motion detection. Accelerometers are being increasingly used in WS systems because they offer advantages: low power consumption; affordability; lightness; ease of use; small size; the potential to be mounted on various body parts; and most importantly, extreme portability. Therefore, in some representative papers [6; 7; 8] a 3-axis accelerometer with the threshold-based algorithm was used. In these papers, the authors detected falls when the acceleration from a 3-axis accelerometer exceeded the threshold. One of the essential advantages of using the threshold-based method is that it is less complex and less computationally intensive than the other methods. However, finding suitable thresholds to detect all types of falls without mislabelling activities of daily living (ADL) has proven to be a complex problem.  
A similar approach uses a smartphone's built-in accelerometer to monitor the movement data of an elderly person continuously. In [9], the collected data were used to test three different learning classifiers offline: decision trees, k-nearest-neighbours (KNN) and naive Bayes. The results show that the decision-trees-based algorithm had the best performance, with more equilibrated sensitivity and specificity values compared with the other algorithms. Nevertheless, due to smartphones' relatively high energy consumption, this system could only be active for a short period.  
Recently, WS systems based on machine learning (ML) approaches have been proposed [10; 11; 12; 13; 14] to address these limitations and improve the accuracy of fall detection. One study [15] used a nonlinear support vector machine to extract features and gain meaning from body data captured by an accelerometer attached to a smart textile. Two feature extractions were required to identify the peak to detect the fall direction, requiring more processing than a single extraction algorithm. The authors of [16] detected and predicted falls using a method based on the hidden Markov model (HMM), which involved gathering time series from the movements obtained by a three-axis accelerometer placed on the upper body. The test results show a perfect success rate of drop detection (100% sensitivity and 100% specificity). However, they used data samples from adolescents' simulated activities to train and adjust the HMM and the system's thresholds.