Smart Lifelogging: Recognizing Human Activities using PHASOR

Smartphones are increasing ubiquitously due to the need and demand in the modern era. The world is transmuting into a global village with the cumulation of smart devices. Nowadays, smartphones are enriched with inertial sensors which can be used to recognize physical human activities in the wild. Human activity recognition (HAR) lies at the core of many applications like health monitoring, fall detection, road safety, personal assistance, and behavior-based context awareness. Context-based HAR is a new dimension that provides fine-grained information about the action being performed and leads us towards the automated and intelligent system design, which is useful to furnish the smart solution to real-life problems. In this paper, smartphone sensors are utilized to propose a framework for human activity and context recognition. This framework performs activity-dependent context recognition on the ExtraSensory dataset using random forest, decision tree, and k-nearest neighbor classifiers, and achieves an accuracy of 93.10%, demonstrating the effectiveness of the proposed framework.

Smartphone-Based Human Activity Recognition Using Bagging and Boosting

In this paper, we present a home-monitoring oriented human activity recognition benchmark database, based on the combination of a color video camera and a depth sensor. Our contributions are two-fold: 1) We have created a publicly releasable human activity video database (i.e., named as RGBD-HuDaAct), which contains synchronized color-depth video streams, for the task of human daily activity recognition. This database aims at encouraging more research efforts on human activity recognition based on multi-modality sensor combination (e.g., color plus depth). 2) Two multi-modality fusion schemes, which naturally combine color and depth information, have been developed from two state-of-the-art feature representation methods for action recognition, i.e., spatio-temporal interest points (STIPs) and motion history images (MHIs). These depth-extended feature representation methods are evaluated comprehensively and superior recognition performances over their uni-modality (e.g., color only) counterparts are demonstrated.

Human activity recognition is concerned with identifying the specific movement of a person based on sensor data. In recent years, many different techniques have been proposed for modelling and recognising human activities, with a specific focus on the development of approaches to classifying human activities using deep learning techniques. The research presented in this paper proposes a fuzzy feature representation approach to represent occupancy sensor data, along with a Convolutional Neural Network Classifier (CNNC) for human activity modelling and recognition. Sensory data gathered from a home environment are converted into occupancy data representing human activities and then fuzzified before being fed as inputs into the CNNC. The learning capability of CNNC allows the model to learn the relationship between the fuzzified inputs and their corresponding output activities during training mode. The relations learned in the trained CNNC model are then used to identify human activity patterns and classify these when the testing dataset is applied. The proposed method is evaluated using a dataset representing activities of daily living for a single user gathered from a real-home environment.

Human activity recognition has an important role in the interaction between human and human relationships because it provides information about a person's identity, personality, activities, psychological state, and health, all this information is difficult to extract due to the difficulty of a person's ability to identify the activities of another person and is considered one of the basic research topics in the scientific fields in the field of computer vision and machine learning. the purpose of human activity recognition (HAR) is to identify the different human activities throw monitoring and register the human activates and the various surrounded environment, by using computers, the human activity recognition researches which depending on visions is the basics of lots of applications even video monitoring or health care and security monitoring and the interaction between the human and the computers. In this research, a review of the newest development in the human activity recognition branch have been studied, and the different ways to recognize the human actions, an important detail have been shown to preview the HAR researches and the methodologies used to represent the human activates and its classifications, to provide an overview of the HAR methods and comparing them

Human activity recognition with smartphone sensors using deep learning neural networks

In this article, human activity recognition (HAR) attempts to recognize the activities of an object in a multistory building from data retrieved via smartphone-based sensors (SBSs). Most publications based on machine learning (ML) report the development of a suitable architecture to improve classification accuracy by increasing the parameters of the architecture pertaining to HAR. Due to robust and automated ML, it is quite possible to develop a versatile approach to improve the accuracy of HAR. This research proposes an optimal ensemble HAR and floor detection (OEC-HAFD) scheme based on automated learning (AutoML) and weighted soft voting (WSF) using SBS to improve the recognition rate. The proposed HAR-FD scheme is developed based on twofold mechanisms. First, an AutoML paradigm is employed to find optimal supervised models based on performance. Second, top-ranked (optimal) models are combined using the WSF mechanism to classify HAR on various floors of a multistory building. The proposed scheme is developed based on real-time SBS: accelerometer and barometer data. The accelerometer data are used to detect activity by observing the magnitude of the sensor measurements. Similarly, the barometer sensor detects floor height by using pressure and altitude data. Furthermore, we analyze the performance of the proposed optimal ensemble classifier (OEC) and compare them with the state-of-the-art classifiers. Based on the result analysis, it is clearly seen that the proposed OEC outperforms the performance of the individual classifiers. Moreover, our proposed HAR-FD can be leveraged as a robust solution to accurately recognize human activities in multistory buildings compared to the existing standalone models.

Human activity recognition plays a unique role in many important applications, including ubiquitous computing, health-care services, and smart buildings. Due to the nonintrusive property of smartphones, smartphone sensors are widely used for the identification of human activities. Since the signals of smartphone sensors are quite noisy, feature engineering will be performed to extract more discriminant representations. Then, various machine learning algorithms can be employed to recognize different human activities. Extreme learning machine (ELM) has been shown to be effective in classification tasks with extremely fast learning speed. Due to its randomness property, it is naturally suitable for ensemble learning. In this paper, we propose a novel ensemble ELM algorithm for human activity recognition using smartphone sensors. Gaussian random projection is employed to initialize the input weights of base ELMs. By doing this, more diversities can be generated to boost the performance of ensemble learning. Real experimental data has been applied to evaluate the performance of our proposed approach. We also conduct a comparison of the proposed approach with some state-of-the-art approaches in the literature. The experimental results indicate that our proposed ensemble ELM approach outperforms these approaches and can achieve recognition accuracies of $\text{97.35}\%$ and $\text{98.88}\%$ on two datasets.

Human activity modeling and recognition using wearable sensors is important in pervasive healthcare, with applications including quantitative assessment of motor function, rehabilitation, and elder care. Previous human activity recognition techniques use a whole-motion model in which continuous sensor streams are divided into windows with a fixed time duration whose length is chosen such that all the relevant information in each activity signal can be extracted from each window. In this paper, we present a statistical motion primitive-based framework for human activity representation and recognition. Our framework is based on Bag-of-Features (BoF), which builds activity models using histograms of primitive symbols. We experimentally validate the effectiveness the BoF-based framework for recognizing nine activity classes and evaluate six factors which impact the performance of the framework. The factors include window size, choices of features, methods to construct motion primitives, motion vocabulary size, weighting schemes of motion primitive assignments, and learning machine kernel functions. Finally, we demonstrate that our statistical BoF-based framework can achieve much better performance compared to a non-statistical string-matching-based approach.

Smartphones and wearable devices are in the frontlines when it comes to the field of Human Activity Recognition (HAR). There have been numerous attempts to use motion sensors in smartphones and wearables to recognize human activity. Most of these studies apply supervised learning techniques, which requires them to use labeled datasets. In this work, we take a sample of these labels, or action primitives (sit, stand, run, walk, jump, lie down), and evaluate them against the resulting labels of several clustering algorithms. We built two datasets (labeled and unlabeled) using accelerometer, gyroscope, and pedometer readings from two fixed-position devices, a smartphone in the side pocket, and a smartwatch strapped onto the left-hand wrist. Ultimately, we want to determine whether these action primitives commonly used in HAR are optimal, and suggest a better set of primitives if not.

In recent years, it has been observed that many researchers have been working on different areas of detection, recognition and monitoring of human activities. The automatic determination of human physical activities is often referred to as human activity recognition (HAR). One of the most important technology that detects and tracks the activity of the human body is sensor-based HAR technology. In recent days, sensor-based HAR attracts attention in the field of computers due to its wide use in daily life and is a rapidly growing field of research. Activity recognition (AR) application is carried out by evaluating the signals obtained from various sensors placed in the human body. In this study, a new approach is proposed to extract features from sensor signals using HAR. The proposed approach is inspired by the Gray Level Co-Occurrence Matrix (GLCM) method, which is widely used in image processing, but it is applied to one-dimensional signals, unlike GLCM. Two datasets were used to test the proposed approach. The datasets were created from the signals obtained from the accelerometer, gyro and magnetometer sensors. Heralick features were obtained from co-occurrence matrix created after 1D-GLCM (One (1) Dimensional-Gray Level Co-Occurrence Matrix) was applied to the signals. HAR operation has been carried out for different scenarios using these features. Success rates of 96.66 and 93.88% were obtained for two datasets, respectively. It has been observed that the new approach proposed within the scope of the study provides high success rates for HAR applications. It is thought that the proposed approach can be used in the classification of different signals.

With the rapid development of the computer and sensor field, inertial sensor data have been widely used in human activity recognition. At present, most relevant studies divide human activities into basic actions and transitional actions, in which basic actions are classified by unified features, while transitional actions usually use context information to determine the category. For the existing single method that cannot well realize human activity recognition, this paper proposes a human activity classification and recognition model based on smartphone inertial sensor data. The model fully considers the feature differences of different properties of actions, uses a fixed sliding window to segment the human activity data of inertial sensors with different attributes and, finally, extracts the features and recognizes them on different classifiers. The experimental results show that dynamic and transitional actions could obtain the best recognition performance on support vector machines, while static actions could obtain better classification effects on ensemble classifiers; as for feature selection, the frequency-domain feature used in dynamic action had a high recognition rate, up to 99.35%. When time-domain features were used for static and transitional actions, higher recognition rates were obtained, 98.40% and 91.98%, respectively.

Along with the development of the Industrial Internet of Things, human activity recognition (HAR) has received widespread attention in many fields. Support Vector Machine (SVM), is widely used by researchers for human activity recognition. However, the inherent difference of signal properties from different sensors and various orientations is potentially lost when using the vector-based SVM for human activity recognition. What's more, the outlier sensitivity problem of the standard SVM reduces the accuracy of human activity recognition. To tackle this problem, we present a tensor-based feature representation model and a weighted support tensor machine (WSTM) for human activity recognition. Specifically, tensor-based representations are first used to model features from different sensors and various orientations to retain the latent relationship. In addition, the weighted support tensor machine is proposed to classify the human activities in tensor space while avoiding the outlier sensitivity problem. Experimental results demonstrate the proposed WSTM algorithm.

Human motion detection and activity recognition are becoming vital for the applications in smart homes. Traditional Human Activity Recognition (HAR) mechanisms use special devices to track human motions, such as cameras (vision-based) and various types of sensors (sensor-based). These mechanisms are applied in different applications, such as home security, Human–Computer Interaction (HCI), gaming, and healthcare. However, traditional HAR methods require heavy installation, and can only work under strict conditions. Recently, wireless signals have been utilized to track human motion and HAR in indoor environments. The motion of an object in the test environment causes fluctuations and changes in the Wi-Fi signal reflections at the receiver, which result in variations in received signals. These fluctuations can be used to track object (i.e., a human) motion in indoor environments. This phenomenon can be improved and leveraged in the future to improve the internet of things (IoT) and smart home devices. The main Wi-Fi sensing methods can be broadly categorized as Received Signal Strength Indicator (RSSI), Wi-Fi radar (by using Software Defined Radio (SDR)) and Channel State Information (CSI). CSI and RSSI can be considered as device-free mechanisms because they do not require cumbersome installation, whereas the Wi-Fi radar mechanism requires special devices (i.e., Universal Software Radio Peripheral (USRP)). Recent studies demonstrate that CSI outperforms RSSI in sensing accuracy due to its stability and rich information. This paper presents a comprehensive survey of recent advances in the CSI-based sensing mechanism and illustrates the drawbacks, discusses challenges, and presents some suggestions for the future of device-free sensing technology.

Enhancing lifestyle and health monitoring of elderly populations using CSA-TkELM classifier