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Author: Xiaoxu Wu
Publisher:
ISBN:
Category :
Languages : en
Pages : 86
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Book Description
The proliferation of powerful microcomputers and the development of modern machine learning tools have enabled human daily activity monitoring systems using wearable inertial sensor like accelerometers and gyroscopes. These systems fulfilled the urgent need in health and wellness industries in helping doctors and clinicians during diagnosis, treatments and rehabilitation processes for neurological diseases like strokes and Parkinson's. For most current activity monitoring systems, there exists an assumption that the sensors are always securely and correctly mounted by the users. Unfortunately, such assumptions do not hold as the scale of studies increase. And it is especially challenging for subjects with neurological diseases to follow instructions about how to mount the sensors everyday, because some of the elderlies tend to be technophobic and neurological diseases are often accompanied with cognitive difficulties. Errors in sensor mounting pose can cause large amount of data loss and distortion and will affect the robustness of the systems severely. In observance of these issues, a series of solutions for sensor orientation and position errors in human motion monitoring and activity classification will be presented. Opportunistic calibration methods to find the true sensor orientation and position will be discussed. In addition, systems that provide robust monitoring regardless of the exact sensor pose will be proposed.
Author: Xiaoxu Wu
Publisher:
ISBN:
Category :
Languages : en
Pages : 86
Get Book Here
Book Description
The proliferation of powerful microcomputers and the development of modern machine learning tools have enabled human daily activity monitoring systems using wearable inertial sensor like accelerometers and gyroscopes. These systems fulfilled the urgent need in health and wellness industries in helping doctors and clinicians during diagnosis, treatments and rehabilitation processes for neurological diseases like strokes and Parkinson's. For most current activity monitoring systems, there exists an assumption that the sensors are always securely and correctly mounted by the users. Unfortunately, such assumptions do not hold as the scale of studies increase. And it is especially challenging for subjects with neurological diseases to follow instructions about how to mount the sensors everyday, because some of the elderlies tend to be technophobic and neurological diseases are often accompanied with cognitive difficulties. Errors in sensor mounting pose can cause large amount of data loss and distortion and will affect the robustness of the systems severely. In observance of these issues, a series of solutions for sensor orientation and position errors in human motion monitoring and activity classification will be presented. Opportunistic calibration methods to find the true sensor orientation and position will be discussed. In addition, systems that provide robust monitoring regardless of the exact sensor pose will be proposed.
Author: Hua-I Chang
Publisher:
ISBN:
Category :
Languages : en
Pages : 117
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Book Description
Enabling large-scale monitoring and classification of a range of motion activities is of primary importance due to the need by healthcare and fitness professionals to monitor exercises for quality and compliance. Video based motion capturing systems (e.g., VICON cameras) provide a partial solution. However, these expensive and fixed systems are not suitable for patients' at-home daily motion monitoring. Wireless motion sensors, including accelerometers and gyroscopes, can provide a low-cost, small-size, and highly-mobile option. However, acquiring robust inference of human motion trajectory via low-cost inertial sensors remains challenging. Sensor noise and drift, sensor placement errors and variation of activity over the population all lead to the necessity of a large amount of data collection. Unfortunately, such a large amount of data collection is prohibitively costly. In observance of these issues, a series of solutions for robust human motion monitoring and activity classification will be presented. The implementation of a real-time context-guided activity classification system will be discussed. To facilitate ground truth data acquisition, we proposed a virtual inertial measurements platform to convert the currently available MoCap database into a noiseless and error-free inertial measurements database. An opportunistic calibration system which deals with sensor placement errors will be discussed. In addition, a sensor fusion approach for robust upper limb motion tracking will also be presented.
Author: Jorge Luis Reyes Ortiz
Publisher: Springer
ISBN: 3319142747
Category : Technology & Engineering
Languages : en
Pages : 147
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Book Description
The book reports on the author’s original work to address the use of today’s state-of-the-art smartphones for human physical activity recognition. By exploiting the sensing, computing and communication capabilities currently available in these devices, the author developed a novel smartphone-based activity-recognition system, which takes into consideration all aspects of online human activity recognition, from experimental data collection, to machine learning algorithms and hardware implementation. The book also discusses and describes solutions to some of the challenges that arose during the development of this approach, such as real-time operation, high accuracy, low battery consumption and unobtrusiveness. It clearly shows that it is possible to perform real-time recognition of activities with high accuracy using current smartphone technologies. As well as a detailed description of the methods, this book also provides readers with a comprehensive review of the fundamental concepts in human activity recognition. It also gives an accurate analysis of the most influential works in the field and discusses them in detail. This thesis was supervised by both the Universitat Politècnica de Catalunya (primary institution) and University of Genoa (secondary institution) as part of the Erasmus Mundus Joint Doctorate in Interactive and Cognitive Environments.
Author: Nimish Rajiv Kale
Publisher:
ISBN:
Category : Dynamic programming
Languages : en
Pages : 172
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Book Description
We describe a body sensor system that detects human activities in real-time. The system consists of wearable computers known as sensor nodes (motes) that can sense information, process them and transmit the results to a Personal Device like Smart phone, PDA or Personal Computer. The motes are attached to different parts of the human body, namely waist and right thigh. Daily living activity monitoring is important in improving quality of life especially in elderly. A wireless wearable network of inertial sensor nodes can be used to observe daily motions. Continuous stream of data generated by these sensor networks can be used to recognize the movements of interest. Dynamic Time Warping (DTW) is a widely used signal processing for time-series pattern matching because of its robustness to variations in time domain and speed as opposed to other template matching methods such as Euclidean Distance. Despite of this flexibility, for the application of activity recognition, DTW can only find the similarity between template of a movement and the incoming samples, when the location and orientation of sensor remains unchanged. Due to this restriction, small sensor misplacements can lead to false classifications. In this work, we adopt DTW distance as a feature for real-time detection of human daily activities like sit to stand. To measure this performance of DTW, we need infinite closely spaced sensors which are impractical. To deal with this problem, we use the marker based optical motion capture system and generate inertial sensor data for different location and orientation on the body. We study the performance of the DTW under these conditions and determine the worst-case sensor location variations, the algorithm can accommodate.
Author: Nobuo Kawaguchi
Publisher: Springer Nature
ISBN: 3030130010
Category : Computers
Languages : en
Pages : 250
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Book Description
Activity recognition has emerged as a challenging and high-impact research field, as over the past years smaller and more powerful sensors have been introduced in wide-spread consumer devices. Validation of techniques and algorithms requires large-scale human activity corpuses and improved methods to recognize activities and the contexts in which they occur. This book deals with the challenges of designing valid and reproducible experiments, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating activity recognition systems in the real world with real users.
Author: Paul Kisik Yoon
Publisher:
ISBN:
Category :
Languages : en
Pages : 62
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Book Description
Recently, miniature inertial measurement units (IMUs) have been deployed as wearable devices to monitor human motion in an ambulatory fashion. This thesis presents a robust human motion tracking algorithm using the IMU and radio-based wireless sensors, such as the Bluetooth Low Energy (BLE) and ultra-wideband (UWB). First, a novel indoor localization method using the BLE and IMU is proposed. The BLE trilateration residue is deployed to adaptively weight the estimates from these sensor modalities. Second, a robust sensor fusion algorithm is developed to accurately track the location and capture the lower body motion by integrating the estimates from the UWB system and IMUs, but also taking advantage of the estimated height and velocity obtained from an aiding lower body biomechanical model. The experimental results show that the proposed algorithms can maintain high accuracy for tracking the location of a sensor/subject in the presence of the BLE/UWB outliers and signal outages.
Author: Md Atiqur Rahman Ahad
Publisher: Springer Nature
ISBN: 9811903611
Category : Technology & Engineering
Languages : en
Pages : 268
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Book Description
This book presents the best-selected research papers presented at the 3rd International Conference on Activity and Behavior Computing (ABC 2021), during 20–22 October 2021. The book includes works related to the field of vision- and sensor-based human action or activity and behavior analysis and recognition. It covers human activity recognition (HAR), action understanding, gait analysis, gesture recognition, behavior analysis, emotion, and affective computing, and related areas. The book addresses various challenges and aspects of human activity recognition—both in sensor-based and vision-based domains. It can be considered as an excellent treasury related to the human activity and behavior computing.
Author: Marco D. Tundo
Publisher:
ISBN:
Category : University of Ottawa theses
Languages : en
Pages :
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Book Description
Monitoring an individual's mobility with a modern smartphone can have a profound impact on rehabilitation in the community. The thesis objective was to develop and evaluate a third-generation Wearable Mobility Monitoring System (WMMS) that uses features from inertial measurement units to categorize activities and determine user changes-of-state in daily living environments. A custom suite of MATLAB® software tools were developed to assess the previous WMMS iteration and aid in third-generation WMMS algorithm construction and evaluation. A rotation matrix was developed to orient smartphone accelerometer components to any three-dimensional reference, to improve accelerometer-based activity identification. A quaternion-based rotation matrix was constructed from an axis-angle pair, produced via algebraic manipulations of acceleration components in the device's body-fixed reference frame. The third-generation WMMS (WMMS3) evaluation was performed on fifteen able-bodied participants. A BlackBerry Z10 smartphone was placed at a participant's pelvis, and the device was corrected in orientation. Acceleration due to gravity and applied linear acceleration signals on a BlackBerry Z10 were then used to calculate features that classify activity states through a decision tree classifier. The software tools were then used for offline data manipulation, feature generation, and activity state prediction. Three prediction sets were conducted. The first set considered a zphone orientation independenty mobility assessment of a person's mobile state. The second set differentiated immobility as sit, stand, or lie. The third prediction set added walking, climbing stairs, and small standing movements classification. Sensitivities, specificities and -Scores for activity categorization and changes-of-state were calculated. The mobile versus immobile prediction set had a sensitivity of 93% and specificity of 97%, while the second prediction set had a sensitivity of 86% and specificity of 97%. For the third prediction set, the sensitivity and specificity decreased to 84% and 95% respectively, which still represented an increase from 56% and 88% found in the previous WMMS. The third-generation WMMS algorithm was shown to perform better than the previous version in both categorization and change-of-state determination, and can be used for rehabilitation purposes where mobility monitoring is required.
Author: Miguel A. Labrador
Publisher: CRC Press
ISBN: 1466588276
Category : Computers
Languages : en
Pages : 209
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Book Description
Learn How to Design and Implement HAR Systems The pervasiveness and range of capabilities of today’s mobile devices have enabled a wide spectrum of mobile applications that are transforming our daily lives, from smartphones equipped with GPS to integrated mobile sensors that acquire physiological data. Human Activity Recognition: Using Wearable Sensors and Smartphones focuses on the automatic identification of human activities from pervasive wearable sensors—a crucial component for health monitoring and also applicable to other areas, such as entertainment and tactical operations. Developed from the authors’ nearly four years of rigorous research in the field, the book covers the theory, fundamentals, and applications of human activity recognition (HAR). The authors examine how machine learning and pattern recognition tools help determine a user’s activity during a certain period of time. They propose two systems for performing HAR: Centinela, an offline server-oriented HAR system, and Vigilante, a completely mobile real-time activity recognition system. The book also provides a practical guide to the development of activity recognition applications in the Android framework.
Author: Yatiraj K Shetty
Publisher:
ISBN:
Category : Human mechanics
Languages : en
Pages : 136
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Book Description
The advancements in the technology of MEMS fabrication has been phenomenal in recent years. In no mean measure this has been the result of continued demand from the consumer electronics market to make devices smaller and better. MEMS inertial measuring units (IMUs) have found revolutionary applications in a wide array of fields like medical instrumentation, navigation, attitude stabilization and virtual reality. It has to be noted though that for advanced applications of motion tracking, navigation and guidance the cost of the IMUs is still pretty high. This is mainly because the process of calibration and signal processing used to get highly stable results from MEMS IMU is an expensive and time-consuming process. Also to be noted is the inevitability of using external sensors like GPS or camera for aiding the IMU data due to the error propagation in IMU measurements adds to the complexity of the system.First an efficient technique is proposed to acquire clean and stable data from unaided IMU measurements and then proceed to use that system for tracking human motion. First part of this report details the design and development of the low-cost inertial measuring system yIMU. This thesis intends to bring together seemingly independent techniques that were highly application specific into one monolithic algorithm that is computationally efficient for generating reliable orientation estimates. Second part, systematically deals with development of a tracking routine for human limb movements. The validity of the system has then been verified.The central idea is that in most cases the use of expensive MEMS IMUs is not warranted if robust smart algorithms can be deployed to gather data at a fraction of the cost. A low-cost prototype has been developed comparable to tactical grade performance for under $15 hardware. In order to further the practicability of this device we have applied it to human motion tracking with excellent results. The commerciality of device has hence been thoroughly established.
