1. INTRODUCTION
These days, we can see a quick increment in people using wearable devices for more different purposes and reasons in their lives. According to the previous studies, the employment of connected wearable detector devices is expected to extend from 325 million in 2016 to 1105 million in 2022 [1]. In this way, where an unmeasurable number of wearable devices is connected together will produce a huge amount of information consistently, at that time, we face or interaction with a critical test of storing, handling, and processing that information’s to produce usable information and to make a keen world. Distinguishing regularities and abnormalities or inconsistencies in gushing information from that measure of usable information, we previously produced subsequent to putting away and preparing it can give us an encounter and can possibly give experiences, and is useful in human services, money, security, web-based social networking, and numerous applications [2], [3].
Over the most recent few years, totally different types of methodologies and techniques have been proposed for identifying regularities and irregularities pattern in streaming data for fall detection, which may be a wearable tool based, ambiance sensor-based, and vision-based [4]. Above all, wearable devices typically take some different benefit of embedded sensors to observe the movement and placement of the body, such as measuring system, accelerometer, magnetometer, and gyroscope [5], [6]. As well as the value of wearable tool based any methodologies or techniques are very low, also because the installation and operation are not complex, and the task is not difficult for the elderly [7], [8]. If from now the physician could not specifically monitoring and identify falls, then there is no chance to save or protect you in future to preventing an accident. For such reasons, fall identification and anticipation have turned into a significant issue then must be find and propose a good way to solve in the method of detecting regularities and irregularities in streaming data for fall detection [9], [10].
Fall in the human daily life is one of the main health risks and dangerous, mostly for the older community in our today’s society, because of the rose growing in mortality, morbidity, incapacity, disability, and frailty [11]. Nearly for fall initiate injuries as collected and represented, over 80% of all damage related clinic confirmations among peoples for more than 65 years [12], [13]. According to the reasons as we mentioned before, falls affect a huge number of the elderly all through the world. For instance, falls some of the aged cost the National Health Service more than £4.6 million every day as indicated by a report by the Centre for Social Justice UK [14].
The notable studies and researches in this filed are detecting fall or anomaly in real streaming data [15]-[20] and outliers [21], [22]. We will probably investigate the circumstance of unpredictable human development identification, for example, fall, by utilizing continuous sensor information. At that point, we map that issue as sporadic example location issue, thinking about a fall as a capricious action with respect to standard human action and attempt to perceive the exceptional fall situations from ordinary development or human activities, for example, walking, sitting, lying (LYI), sleeping, standing, and every other movement such as playing, cooking, and running.
In general, Artificial Neural Networks (ANN) have systematically achieved higher results in the detection falls from physical activity observation knowledge. Ozdemir and Barshan have used a pair of 2520 trials to make a huge amount of dataset [23]. Their fall detection system achieved 95% accuracy by employing a multi-layer perceptron (MLP) for binary classification between activities of daily living (ADL) and fall. Kerdegari et al. recorded 1000 movement acceleration data collection using a waist-worn measuring system and obtained 91.6% accuracy for binary classification for ADL against fall using MLP [24]. Nukala et al. collected knowledge from 322 tests achieved 98.7% accuracy with MLP victimization scaled conjugate graduate learning [25].
Theodoridis et al. [26] developed two long short-term memory (LSTM) models, one with easy measuring system data collection associated another with accelerometer data revolved at an angle, using a published dataset referred to as UR Fall Detection. The LSTM model with rotation obtained the most effective results with 98.57% accuracy.
The work by class Musci et al. used one public dataset (SisFall) dataset implementing Recurrent Neural Network with underlying LSTM blocks for developing online fall detection system [27]. They achieved 97.16% accuracy victimization associate best window breadth of w = 256. Ajerla et al. [42] slightly changed the preprocessing done by Vavoulas et al. on other public dataset (MobiAct) dataset and achieved quite 90% accuracies on most of their experiments victimization MLP and LSTM [28], [29]. They also obtained 99% accuracy victimization LSTM in two of their experiments. Table 1 shows the compares performance of the ANN techniques.
TABLE 1 Comparison of the ANN techniques
Nowadays, there are a lot of wearable sensors devices exist which may observe falls automatically and send a notification to the caregivers, machine services, or ambulance offerings. However, most of them are expensive or need a subscription of month-to-month service. A large number of studies and research has been done on fall detection the usage of sensors such as accelerometers and gyroscopes, because of low cost and incorporated into a large number of cell phones accessible todays in the world such as smartphones and tablets. Rather than fall identification, there is a dire need for prediction and prevention systems [30], [31].
In this paper, we propose a system for detecting abnormal pattern of falling behavior in real-time streaming data in internet of things (IoT). The data were obtained from wearable sensor systems used for human health and activity tracking and control. In our research, we focus on how to train the proposed system to recognize and distinguish irregular activity pattern related to specific kinds of fall according to three different annotated published datasets: MobiAct, using Vavoulas et al. in 2016 [32], SisFall, using Sucerquia et al. in 2017 [33], and UMAFALL by way of Eduardo et al. in 2018 [34] for implementing our framework from streaming data for fall detection, we used an ANN model for fall recognition giving (96.71%) accuracy. At that point, we integrate our system for that huge amount of data recordings from streaming sensor (online) into free IBM Streams to fabricate an IBM Cloud-based IoT information preparing structure.