Detecting Steps Walking at very Low Speeds Combining Outlier Detection, Transition Matrices and Autoencoders from Acceleration Patterns

In this paper, we develop and validate a new algorithm to detect steps while walking at speeds between 30 and 40 steps per minute based on the data sensed from a single tri-axial accelerometer. The algorithm concatenates three consecutive phases. First, an outlier detection is performed on the sensed data based on the Mahalanobis distance to pre-detect candidate points in the acceleration time series that may contain a ground contact segment of data while walking. Second, the acceleration segment around the pre-detected point is used to calculate the transition matrix in order to capture the time dependencies. Finally, autoencoders, trained with data segments containing ground contact transition matrices from acceleration series from labeled steps are used to reconstruct the computed transition matrices at each pre-detected point. A similarity index is used to assess if the pre-selected point contains a true step in the 30-40 steps per minute speed range. Our experimental results, based on a database from three different participants performing similar activities to the target one, are able to achieve a recall = 0.88 with precision = 0.50 improving the results when directly applying the autoencoders to acceleration patterns (recall = 0.77 with precision = 0.50)

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

Wearable and Nearable Biosensors and Systems for Healthcare

Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices

Patient Movement Monitoring Based on IMU and Deep Learning

Osteoarthritis (OA) is the leading cause of disability among the aging population in the United States and is frequently treated by replacing deteriorated joints with metal and plastic components. Developing better quantitative measures of movement quality to track patients longitudinally in their own homes would enable personalized treatment plans and hasten the advancement of promising new interventions. Wearable sensors and machine learning used to quantify patient movement could revolutionize the diagnosis and treatment of movement disorders. The purpose of this dissertation was to overcome technical challenges associated with the use of wearable sensors, specifically Inertial Measurement Units (IMUs), as a diagnostic tool for osteoarthritic (OA) and total knee replacement patients (TKR) through a detailed biomechanical assessment and development of machine learning algorithms. Specifically, the first study developed a relevant dataset consisting of IMU and associated biomechanical parameters of OA and TKR patients performing various activities, created a machine learning-based framework to accurately estimate spatiotemporal movement characteristics from IMU during level ground walking, and defined optimum sensor configuration associated with the patient population and activity. The second study designed a framework to generate synthetic kinematic and associated IMU data as well as investigated the influence of adding synthetic data into training-measured data on deep learning model performance. The third study investigated the kinematic variation between two patient’s population across various activities: stair ascent, stair descent, and gait using principle component analysis PCA. Additionally, PCA-based autoencoders were developed to generate synthetic kinematics data for each patient population and activity. The fourth study investigated the potential use of a universal deep learning model for the estimation of lower extremities’ kinematics across various activities. Therefore, this model can be used as a global model for transfer learning methods in future research. This line of study resulted in a machine-learning framework that can be used to estimate biomechanical movements based on a stream of signals emitted from low-cost and portable IMUs. Eventually, this could lead to a simple clinical tool for tracking patients\u27 movements in their own homes and translating those movements into diagnostic metrics that clinicians will be able to use to tailor treatment to each patient\u27s needs in the future

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

Predictive Models For Falls-Risk Assessment in Older People, Using Markerless Motion Capture

Falling in old age contributes to considerable misery for many people. Currently, there is a lack of practical, low cost and objective methods for identifying those at risk of falls. This thesis aims to address this need. The majority of the literature related to falls risk and balance impairment uses force plates to quantify postural sway. The use of such devices in a clinical setting is rare, mainly due to cost. However, some force-plate-based commercial products have been created, e.g. the Balance Master. To align the research in this thesis to both the literature and existing methods of assessing postural sway, a method is proposed which can generate sway metrics from the output of a low-cost markerless motion capture device (Kinect V2). Good agreement was found between the proposed method and the output of the Balance Master. A key reason for the lack of research into falls-risk using markerless motion capture, is the lack of an appropriate dataset. To address this issue, a dataset of clinical movements, recorded using markerless motion capture, was created. Named KINECAL, It contains the recordings of 90 participants, labelled by age and falls-risk. The data provided includes depth images, 3D joint positions, sway metrics and socioeconomic and health meta data. Many studies have noted that postural sway increases with age and conflate age-related changes with falls risk. However, if one examines sub-populations of older people, such as master athletes, It is clear that this is not necessarily true. The structure of KINECAL allows for the examination of age-related factors and falls-risk factors simultaneously. In addition, it includes labels of falls history, clinical impairment and comprehensive metadata. KINECAL was used to identify sway metrics most closely associated with falls risk, as distinct from the ageing process. Using the identified metrics, a model was developed that can identify those who would be classified as impaired by a range of clinical tests. Finally, a model is proposed, which can predict fallers by placing individuals on a scale of physical impairment. An autoencoder was used to model, healthy adult sit-to-stand movements. Using an anomaly detection approach, an individuals level of impairment can be plotted relative to this healthy standard. Using this model, the existence of two older populations (one with a high falls risk and one with a low falls risk) is demonstrated

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Data-Driven Representation Learning in Multimodal Feature Fusion

abstract: Modern machine learning systems leverage data and features from multiple modalities to gain more predictive power. In most scenarios, the modalities are vastly different and the acquired data are heterogeneous in nature. Consequently, building highly effective fusion algorithms is at the core to achieve improved model robustness and inferencing performance. This dissertation focuses on the representation learning approaches as the fusion strategy. Specifically, the objective is to learn the shared latent representation which jointly exploit the structural information encoded in all modalities, such that a straightforward learning model can be adopted to obtain the prediction. We first consider sensor fusion, a typical multimodal fusion problem critical to building a pervasive computing platform. A systematic fusion technique is described to support both multiple sensors and descriptors for activity recognition. Targeted to learn the optimal combination of kernels, Multiple Kernel Learning (MKL) algorithms have been successfully applied to numerous fusion problems in computer vision etc. Utilizing the MKL formulation, next we describe an auto-context algorithm for learning image context via the fusion with low-level descriptors. Furthermore, a principled fusion algorithm using deep learning to optimize kernel machines is developed. By bridging deep architectures with kernel optimization, this approach leverages the benefits of both paradigms and is applied to a wide variety of fusion problems. In many real-world applications, the modalities exhibit highly specific data structures, such as time sequences and graphs, and consequently, special design of the learning architecture is needed. In order to improve the temporal modeling for multivariate sequences, we developed two architectures centered around attention models. A novel clinical time series analysis model is proposed for several critical problems in healthcare. Another model coupled with triplet ranking loss as metric learning framework is described to better solve speaker diarization. Compared to state-of-the-art recurrent networks, these attention-based multivariate analysis tools achieve improved performance while having a lower computational complexity. Finally, in order to perform community detection on multilayer graphs, a fusion algorithm is described to derive node embedding from word embedding techniques and also exploit the complementary relational information contained in each layer of the graph.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Radar based discrete and continuous activity recognition for assisted living

In an era of digital transformation, there is an appetite for automating the monitoring process of motions and actions by individuals who are part of a society increasingly getting older on average. ”Activity recognition” is where sensors use motion information from participants who are wearing a wearable sensor or are in the field of view of a remote sensor which, coupled with machine learning algorithms, can automatically identify the movement or action the person is undertaking. Radar is a nascent sensor for this application, having been proposed in literature as an effective privacy-compliant sensor that can track movements of the body effectively. The methods of recording movements are separated into two types where ’Discrete’ movements provide an overview of a single activity within a fixed interval of time, while ’Continuous’ activities present sequences of activities performed in a series with variable duration and uncertain transitions, making these a challenging and yet much more realistic classification problem. In this thesis, first an overview of the technology of continuous wave (CW) and frequency modulated continuous wave (FMCW) radars and the machine learning algorithms and classification concepts is provided. Following this, state of the art for activity recognition with radar is presented and the key papers and significant works are discussed. The remaining chapters of this thesis discuss the research topics where contributions were made. This is commenced through analysing the effect of the physiology of the subject under test, to show that age can have an effect on the radar readings on the target. This is followed by porting existing radar recognition technologies and presenting novel use of radar based gait recognition to detect lameness in animals. Reverting to the human-centric application, improvements to activity recognition on humans and its accuracy was demonstrated by utilising features from different domains with feature selection and using different sensing technologies cooperatively. Finally, using a Bi-long short term memory (LSTM) based network, improved recognition of continuous activities and activity transitions without human-dependent feature extraction was demonstrated. Accuracy rate of 97% was achieved through sensor fusion and feature selection for discrete activities and for continuous activities, the Bi-LSTM achieved 92% accuracy with a sole radar sensor