# Activity Recognition Using Different Sensor Modalities and Deep Learning
## 1 Introduction  
Nowadays, immobility is one of the major problems that adversely affects life quality. It may cause falling, various syndromes, and physical disabilities. The World Health Organization informs us that approximately 28-35% of people above 65 years fall at least once each year [1]. Reducing the risk is possible by performing regular physical activities such as walking, playing sports, or cycling. However, people can forget or ignore daily activities due to work pace or lifestyle. To prevent possible health problems, especially in the aging stage, the monitoring and recognition of activities are highly important. There are two popular methods to monitor activities. Video-based systems are the most reliable but have some disadvantages such as cost, need for a special environment, and useful for only indoor applications [2, 3]. Moreover, they suffer from issues such as privacy and ethics [4]. With the rapid advances in sensor and connectivity technologies, wearable systems such as smartphones, smartwatches, smart insoles, and smart clothes allow for monitoring daily movements with superior advantages including low-cost, lightweight,and small size [5]. Moreover, they enable continuous monitoring with low power consumption, even if the user is in an outdoor environment. Many researchers have presented a variety of wireless wearable devices, especially using inertial measurement unit (IMU) sensors, force-sensitive resistors (FSRs), and electromyography [6; 7; 8; 9]. [PERSON] et al. designed a kinetic insole consisting of FSRs and IMU sensors for physical therapy and athletic training [10]. [PERSON] et al. developed a wearable device with an accelerometer, gyroscope, magnetometer, and barometer sensor to detect falls in elderly people [11]. [PERSON] et al. built up a wearable system including inertial sensing modules to recognize sports activities [12]. [PERSON] et al. proposed a smart insole based on pressure sensors for fall detection [13]. [PERSON] et al. designed a wearable system based on air pressure and inertial measurement unit sensors to improve the performance of human activity recognition (HAR) [14]. Moreover, smartphones and smartwatches including IMU sensors have been used as wearable devices recently for activity recognition [15; 16]. In the literature, traditional and modern machine learning techniques have been used for human activity recognition. Traditional machine learning is based on hand-crafted methods. The features are extracted from datasets manually using techniques such as statistical information, time-frequency transformation, etc., and then machine learning algorithms are applied. With this method; [PERSON] et al. compared the performances of five algorithms including a k-nearest neighbor, feedforward neural network, support vector machines, naive Bayes, and decision tree for activity recognition. They extracted 342 features from the input set and reduced them to 69 features using genetic algorithms and the correlation technique [17]. [PERSON] et al. proposed the classification of six basic activities by using a support vector machine, hybrid versions of the support vector machine and a hidden Markov model, and artificial neural networks. They extracted 561 features [18]. However, the generalization capability of the handcrafted method is based on human experience and it causes a waste of time. Recently, deep learning has become a popular and powerful tool with automated feature extraction capability. This method does not need specialized knowledge or expertise and extracts the features from hierarchical architectures. The most popular ones are convolutional neural networks (CNN) and long-short-term neural networks (LSTM NN). CNN is actively used in big data problems. The key factor to CNN's success is its architectural design, which includes convolutional, pooling, normalizing, and fully connected layers that extract semantic outputs from the input data.