# Cross-Platform Gait Analysis and Fall Detection Wearable Device
## 1 Introduction  
In Taiwan, an analysis report from the Health Promotion Administration, HPA, showed that the rehabilitation treatment is more important in modern medicine. It also showed that the fall accident becomes the second accident among all accidents for the elders more than sixty-five years in Taiwan due to significant decline for controlling and balancing muscle, such as gait analysis [1; 2; 3; 4; 5]. In Ref. [6], it developed a wearable Freezing of Gait (FOG) collection device for wavelet analysis on shank sagittal velocity signals and a synchronization of loss threshold (SLT) for prediction of FOG in people with Parkinson's Disease (PD). In Ref. [7], it proposed a wearable foot gait collection device with step direction algorithm for motion state and direction of feet exercising. By using this wearable device, the burnt calories could be calculated. The health state of user also could be observed at any time and place by using this wearable foot gait collection device. In Ref. [8], a piecewise linear labeling was proposed to calculate the angular positions and velocities of thigh and torso segments as the training model data based on the variable toe-off onset with different walking speed. By the proposed piecewise linear labeling, the speed adaptability of gait phase estimation could be improved. Estimation accuracy of gait phase could be improved. In Ref. [9], it showed that the risk of fatal falls for elderly people is getting important. The Centers for Disease Control and Prevention (CDC) also showed that the serious injuries of elderly people may be occurred even if the falling is innocent. Hence, a system with wearable fall detection devices for tracking patient's whole activity by accelerometer sensors was proposed. The patient's predetermined alert could be identified and the corresponding alerts could be sent to the patients. Devices for tracking motion with high performance, low power, and low cost included the smart wearable sensors. Data could be decided based on data processing unit. In Ref. [10], it showed that the fatality may be occurred by fall, especially for elderly people who live alone. Hence, this paper aimed to detect fall by supervised machine learning (ML) algorithms, such as decision tree (DT), k-Nearest Neighbour (k-NN), and support vector machine (SVM). The experimental results showed that the sensitivity and accuracy of DT is better than _those_ of k-NN and SVM. In Ref. [11], it addressed the necessity of human fall elements by wearable devices regrading falls included signals acquired, features extracted, and algorithms, jointly. In Ref. [12], it proposed a new data augmentation application for different rotation errors in wearable fall detection sensors. The above mention showed the importance of gait analysis and fall detection. Most of gait analysis was suggested to be executed in any place, such as home, by medical staff to save time and money for patients. Although the gait analysis at home could reduce the time, money, and manpower for patients and medical institutions, how to ensure the motion of gait analysis is correct becomes the first important issue without any medical staff. Moreover, how to upload the data of gait analysis to cloud database and detect the fall accident in real time by APP was another issue. A cross-platform gait analysis and fall detection wearable device (CPGAFDWD) was thus proposed firstly in this paper. Most of wearable devices were used by APP in mobile device, such as mobile phone. Since the platform of mobile phone may be different, such as iOS and Android, to ensure the APP we designed could be applied for different kinds of platforms is needed. Hence, a cross-platform APP was proposed to be integrated for CPGAFDWD in this paper. Therefore, a cross-platform gait analysis and fall detection wearable device (CPGAFDWD) was proposed to address the above issues in this paper. The architecture of CPGAFDWD was shown in Figure 1. The user interface of cross-platform APP was shown in Figure 2.  
Figure 1: Architecture of CPGAFDWD.