## 2 Literature Review  
Researchers have produced some frameworks and approaches that can recognize physical activities. Some of the activity recognition and monitoring frameworks is based on the smartwatches and multiple sensors worn on different body positions [4; 32]. Some of these frameworks evaluate the classification accuracy of on-body sensor devices on different body positions [21; 33; 34; 35]. Some frameworks estimate different time-domain features, sampling rates and analyze the trade-off between recognition accuracy and computational complexity [18]. The physical activity recognition with tri-axial sensors has gained more attention. By considering the effect of flexibility in orientation and placement, authors in [36] proposed an approach based on the coordinate transformation, PCA, and SVM. Authors in [33] proposed an approach for dynamic feature extraction and showed that feature selection is important for accurate recognition. They analyzed that Convolutional Neural Networks (CNN) gives better recognition results with dynamic features. In [37], the authors proposed an Ensemble Learning Machine (ELM) algorithm. According to the authors, ELM is the fastest algorithm to train for activity recognition and promising result. Authors in [18] proposed a methodology that utilizes data accumulated from an accelerometer to test it in a non-controlled condition. This investigation was particularly completed for old participants to improve their everyday life exercises. An older simulation unit was built that utilized three android cell phones that were put at the client's midsection, knee, and lower leg. Authors in [38] proposed a low-pass recognition system to recognize human activity. A set of five classifiers used was to classify physical activities using statistical features. Four participants were selected to collect the data at a rate of 100 samples per second (100/s) for 180-280 s. They selected a sliding window of 128 samples of each participant. They also showed different evaluation measures on a single classifier with a combination of other classifiers for further analysis. Authors in [39] used two accelerometers. They performed an offline evaluation of the dataset containing activities and different types of falls. Researchers used a combination of sensors with an accelerometer for activity recognition. Authors in [4], used \"eWatch\" for data collection of six activities. Each \"eWatch\" device comprises of a bi-axial accelerometer and a light sensor. For training, they used different inductive learning models such as Decision Trees, k-Nearest Neighbor, Naive Bayes, and Bayes Net and evaluated their approach using five-fold cross-validation. Authors in [40; 41; 42; 43; 44; 45; 46] discussed the network protocol to use and process the sensors and wearable devices data over the network. The research [13] proposed an assisted approach that helps the participants to live healthily. The system recognizes physical activities (sleeping, walking and running) and suggests an optimal health care plan to participants with the help of doctors, guardians, and intelligent agent rankers. In [33], the authors proposed an approach that extracts dynamic features from time-series human activity data by using recurrence plots [47]. They analyzed the dynamic vs. static features for activity recognition. First, the recurrence plots of each time series from gyroscope and accelerometer sensors were considered as dynamic features for activity recognition then Convolutional Neural Network (CNN) [48] was used for activity recognition. The study [49], present a smart-phone inertial sensors-based approach in which efficient features such as mean, auto-regressive coefficients, median, etc. extracted from raw data and then pass through Kernel Principal Component Analysis (KPCA) and Linear Discriminant Analysis (LDA) for dimension reduction. Finally, the Deep Belief Network (DBN) was used for activity recognition and was compared with Support Vector Machine (SVM) and Artificial Neural Network (ANN).