Outlier Detection in Wearable Sensor Data for Human Activity Recognition (HAR) Based on DRNNs

Wearable sensors provide a user-friendly and non-intrusive mechanism to extract user-relateddata that paves the way to the development of personalized applications. Within those applications, humanactivity recognition (HAR) plays an important role in the characterization of the user context. Outlierdetection methods focus on finding anomalous data samples that are likely to have been generated by adifferent mechanism. This paper combines outlier detection and HAR by introducing a novel algorithmthat is able both to detect information from secondary activities inside the main activity and to extract datasegments of a particular sub-activity from a different activity. Several machine learning algorithms havebeen previously used in the area of HAR based on the analysis of the time sequences generated by wearablesensors. Deep recurrent neural networks (DRNNs) have proven to be optimally adapted to the sequentialcharacteristics of wearable sensor data in previous studies. A DRNN-based algorithm is proposed in thispaper for outlier detection in HAR. The results are validated both for intra- and inter-subject cases and bothfor outlier detection and sub-activity recognition using two different datasets. A first dataset comprising4 major activities (walking, running, climbing up, and down) from 15 users is used to train and validatethe proposal. Intra-subject outlier detection is able to detect all major outliers in the walking activity in thisdataset, while inter-subject outlier detection only fails for one participant executing the activity in a peculiarway. Sub-activity detection has been validated by finding out and extracting walking segments present inthe other three activities in this dataset. A second dataset using four different users, a different setting anddifferent sensor devices is used to assess the generalization of results.This work was supported by the ‘‘ANALYTICS USING SENSOR DATA FOR FLATCITY’’ Project (MINECO/ ERDF, EU) funded in partby the Spanish Agencia Estatal de Investigación (AEI) under Grant TIN2016-77158-C4-1-R and in part by the European RegionalDevelopment Fund (ERDF)

Evaluating the Impact of a Two-Stage Multivariate Data Cleansing Approach to Improve to the Performance of Machine Learning Classifiers: A Case Study in Human Activity Recognition

Human activity recognition (HAR) is a popular field of study. The outcomes of the projects in this area have the potential to impact on the quality of life of people with conditions such as dementia. HAR is focused primarily on applying machine learning classifiers on data from low level sensors such as accelerometers. The performance of these classifiers can be improved through an adequate training process. In order to improve the training process, multivariate outlier detection was used in order to improve the quality of data in the training set and, subsequently, performance of the classifier. The impact of the technique was evaluated with KNN and random forest (RF) classifiers. In the case of KNN, the performance of the classifier was improved from 55.9% to 63.59%

Ensemble residual network-based gender and activity recognition method with signals

Nowadays, deep learning is one of the popular research areas of the computer sciences, and many deep networks have been proposed to solve artificial intelligence and machine learning problems. Residual networks (ResNet) for instance ResNet18, ResNet50 and ResNet101 are widely used deep network in the literature. In this paper, a novel ResNet-based signal recognition method is presented. In this study, ResNet18, ResNet50 and ResNet101 are utilized as feature extractor and each network extracts 1000 features. The extracted features are concatenated, and 3000 features are obtained. In the feature selection phase, 1000 most discriminative features are selected using ReliefF, and these selected features are used as input for the third-degree polynomial (cubic) activation-based support vector machine. The proposed method achieved 99.96% and 99.61% classification accuracy rates for gender and activity recognitions, respectively. These results clearly demonstrate that the proposed pre-trained ensemble ResNet-based method achieved high success rate for sensors signals. © 2020, Springer Science+Business Media, LLC, part of Springer Nature

Review of Wearable Devices and Data Collection Considerations for Connected Health

Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer’s physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient’s functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices

Deep Learning Techniques for Radar-Based Continuous Human Activity Recognition

Human capability to perform routine tasks declines with age and age-related problems. Remote human activity recognition (HAR) is beneficial for regular monitoring of the elderly population. This paper addresses the problem of the continuous detection of daily human activities using a mm-wave Doppler radar. In this study, two strategies have been employed: the first method uses un-equalized series of activities, whereas the second method utilizes a gradient-based strategy for equalization of the series of activities. The dynamic time warping (DTW) algorithm and Long Short-term Memory (LSTM) techniques have been implemented for the classification of un-equalized and equalized series of activities, respectively. The input for DTW was provided using three strategies. The first approach uses the pixel-level data of frames (UnSup-PLevel). In the other two strategies, a convolutional variational autoencoder (CVAE) is used to extract Un-Supervised Encoded features (UnSup-EnLevel) and Supervised Encoded features (Sup-EnLevel) from the series of Doppler frames. The second approach for equalized data series involves the application of four distinct feature extraction methods: i.e., convolutional neural networks (CNN), supervised and unsupervised CVAE, and principal component Analysis (PCA). The extracted features were considered as an input to the LSTM. This paper presents a comparative analysis of a novel supervised feature extraction pipeline, employing Sup-ENLevel-DTW and Sup-EnLevel-LSTM, against several state-of-the-art unsupervised methods, including UnSUp-EnLevel-DTW, UnSup-EnLevel-LSTM, CNN-LSTM, and PCA-LSTM. The results demonstrate the superiority of the Sup-EnLevel-LSTM strategy. However, the UnSup-PLevel strategy worked surprisingly well without using annotations and frame equalization

IMU sensing–based Hopfield neuromorphic computing for human activity recognition

Aiming at the self-association feature of the Hopfield neural network, we can reduce the need for extensive sensor training samples during human behavior recognition. For a training algorithm to obtain a general activity feature template with only one time data preprocessing, this work proposes a data preprocessing framework that is suitable for neuromorphic computing. Based on the preprocessing method of the construction matrix and feature extraction, we achieved simplification and improvement in the classification of output of the Hopfield neuromorphic algorithm. We assigned different samples to neurons by constructing a feature matrix, which changed the weights of different categories to classify sensor data. Meanwhile, the preprocessing realizes the sensor data fusion process, which helps improve the classification accuracy and avoids falling into the local optimal value caused by single sensor data. Experimental results show that the framework has high classification accuracy with necessary robustness. Using the proposed method, the classification and recognition accuracy of the Hopfield neuromorphic algorithm on the three classes of human activities is 96.3%. Compared with traditional machine learning algorithms, the proposed framework only requires learning samples once to get the feature matrix for human activities, complementing the limited sample databases while improving the classification accuracy

Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures

The demographic shift of the population towards an increase in the number of elderly citizens, together with the sedentary lifestyle we are adopting, is reflected in the increasingly debilitated physical health of the population. The resulting physical impairments require rehabilitation therapies which may be assisted by the use of wearable sensors or body area network sensors (BANs). The use of novel technology for medical therapies can also contribute to reducing the costs in healthcare systems and decrease patient overflow in medical centers. Sensors are the primary enablers of any wearable medical device, with a central role in eHealth architectures. The accuracy of the acquired data depends on the sensors; hence, when considering wearable and BAN sensing integration, they must be proven to be accurate and reliable solutions. This book is a collection of works focusing on the current state-of-the-art of BANs and wearable sensing devices for physical rehabilitation of impaired or debilitated citizens. The manuscripts that compose this book report on the advances in the research related to different sensing technologies (optical or electronic) and body area network sensors (BANs), their design and implementation, advanced signal processing techniques, and the application of these technologies in areas such as physical rehabilitation, robotics, medical diagnostics, and therapy

Low-power neuromorphic sensor fusion for elderly care

Smart wearable systems have become a necessary part of our daily life with applications ranging from entertainment to healthcare. In the wearable healthcare domain, the development of wearable fall recognition bracelets based on embedded systems is getting considerable attention in the market. However, in embedded low-power scenarios, the sensor’s signal processing has propelled more challenges for the machine learning algorithm. Traditional machine learning method has a huge number of calculations on the data classification, and it is difficult to implement real-time signal processing in low-power embedded systems. In an embedded system, ensuring data classification in a low-power and real-time processing to fuse a variety of sensor signals is a huge challenge. This requires the introduction of neuromorphic computing with software and hardware co-design concept of the system. This thesis is aimed to review various neuromorphic computing algorithms, research hardware circuits feasibility, and then integrate captured sensor data to realise data classification applications. In addition, it has explored a human being benchmark dataset, which is following defined different levels to design the activities classification task. In this study, firstly the data classification algorithm is applied to human movement sensors to validate the neuromorphic computing on human activity recognition tasks. Secondly, a data fusion framework has been presented, it implements multiple-sensing signals to help neuromorphic computing achieve sensor fusion results and improve classification accuracy. Thirdly, an analog circuits module design to carry out a neural network algorithm to achieve low power and real-time processing hardware has been proposed. It shows a hardware/software co-design system to combine the above work. By adopting the multi-sensing signals on the embedded system, the designed software-based feature extraction method will help to fuse various sensors data as an input to help neuromorphic computing hardware. Finally, the results show that the classification accuracy of neuromorphic computing data fusion framework is higher than that of traditional machine learning and deep neural network, which can reach 98.9% accuracy. Moreover, this framework can flexibly combine acquisition hardware signals and is not limited to single sensor data, and can use multi-sensing information to help the algorithm obtain better stability

Exploring machine learning, real-time bio-feedback, and inertial sensor accuracy for the prevention of running-related injuries

Recreational running is popular, however, incident rates of running related injuries (RRIs) are very high. Predisposition to injury can be assessed through expensive, laboratory-based biomechanical screening. Wearable wireless inertial sensors offer a potential solution, but accurate orientation data are required. This thesis examined the prevention of RRIs, by aiming to improve sensor accuracy, and investigate applications of biofeedback and machine learning. This thesis explored improving (magnetometer-free) orientation accuracy during running, through examination of (i) Z-axis de-drifting, (ii) data-loss (iii) and modifications to the Madgwick filter. Despite some accuracy improvements (i, iii), overall errors were unsuitable for running based applications. Impact loading is associated with RRIs, with thigh angle (quasi-measure of knee-flexion) potentially important in load attenuation. Loading can be altered directly (loading-based biofeedback) or indirectly (technique-based biofeedback), these two types of biofeedback were compared. A mobile phone application was developed providing audio biofeedback to reduce impact accelerations and encourage a ‘softer’ running technique. Both types of feedback reduced loading at the tibia and sacrum, however, tibia loading reduced better with impact accelerations biofeedback, and sacrum loading with thigh angle biofeedback. It would be beneficial to identify runners who may be predisposed to injury. Seven supervised machine learning models were developed to identify runners who may be likely to sustain RRIs, using inertial, kinematic and clinical data collected on 150 prospectively tracked runners. These models resulted in weak predictive accuracy (0.58-0.61 AUC). As we cannot identify runners predisposed to injury, all runners must be recommended for injury prevention interventions. Orientation accuracy was found to be sufficient for relative measures of running technique in the biofeedback app. Future work could investigate biofeedback app use in relation to reduction of RRIs. Additionally, running injury prediction could be examined further with respect to extracting different features (continuous measures) or predicting specific injuries