@article{nokey,

title = {A Framework for Infectious Disease Monitoring With Automated Contact Tracing—A Case Study of COVID-19},

author = {Fang Ye and Sumit Majumder and Wei Jiang and Xiaohe Li and Narayanaswamy Balakrishnan and Yuanting Zhang and M. Jamal Deen},

doi = {10.1109/JIOT.2022.3199216},

year  = {2022},

date = {2022-08-16},

journal = {IEEE Internet of Things Journal},

volume = {10},

issue = {1},

pages = {144-161},

abstract = {Throughout human history, deadly infectious diseases emerged occasionally. Even with the present-day advanced healthcare systems, the COVID-19 has caused more than six million deaths worldwide (as of 27 July 2022). Currently, researchers are working to develop tools for better and effective management of the pandemic. “Contact tracing” is one such tool to monitor and control the spread of the disease. However, manual contact tracing is labor-intensive and time-consuming. Therefore, manually tracking all potentially infected individuals is a great challenge, especially for an infectious disease like COVID-19. To date, many digital contact tracing applications were developed and used globally to restrain the spread of COVID-19. In this work, we perform a detailed review of the current digital contact tracing technologies. We mention some of their key limitations and propose a fully integrated system for contact tracing of infectious diseases using COVID-19 as a case study. Our system has four main modules—1) case maps; 2) exposure detection; 3) screening; and 4) health indicators that take multiple inputs like users’ self-reported information, measurement of physiological parameters, and information of the confirmed cases from the public health, and keeps a record of contact histories using Bluetooth technology. The system can potentially evaluate the users’ risk of getting infected and generate notifications to alert them about the exposure events, risk of infection, or abnormal health indicators. The system further integrates the Web-based information on confirmed COVID-19 cases and screening tools, which potentially increases the adoption rate of the system.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A Survey on Wireless Device-free Human Sensing: Application Scenarios, Current Solutions, and Open Issues},

author = {Jiang Xiao and Huichuwu Li and Minrui Wu and Hai Jin and M. Jamal Deen and Jiannong Cao},

url = {https://dl.acm.org/doi/10.1145/3530682},

issn = {0360-0300},

year  = {2022},

date = {2022-04-19},

journal = {ACM Computing Surveys (CSUR)},

volume = {55},

number = {88},

issue = {5},

pages = {1-35},

abstract = {In the last decade, many studies have significantly pushed the limits of wireless device-free human sensing (WDHS) technology and facilitated various applications, ranging from activity identification to vital sign monitoring. This survey presents a novel taxonomy that classifies the state-of-the-art WDHS systems into 11 categories according to their sensing task type and motion granularity. In particular, existing WDHS systems involve three primary sensing task types. The first type, behavior recognition, is a classification problem of recognizing predefined meaningful behaviors. The second type is movement tracking, monitoring the quantitative values of behavior states integrating with spatiotemporal information. The third type, user identification, leverages the unique features in behaviors to identify who performs the movements. The selected papers in each sensing task type can be further divided into sub-categories according to their motion granularity. Recent advances reveal that WDHS systems within a particular granularity follow similar challenges and design principles. For example, fine-grained hand recognition systems target extracting subtle motion-induced signal changes from the noisy signal responses, and their sensing areas are limited to a relatively small range. Coarse-grained activity identification systems need to overcome the interference of other moving objects within the room-level sensing range. A novel research framework is proposed to help to summarize WDHS systems from methodology, evaluation performance, and design goals. Finally, we conclude with several open issues and present the future research directions from the perspectives of data collection, sensing methodology, performance evaluation, and application scenario.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A Tensor-Based Computing and Optimization Model for Intelligent Edge Services},

author = {Xiaokang Wang and Laurence T. Yang and Lei Ren and Yihao Wang and M. Jamal Deen},

doi = {10.1109/MNET.011.1800508},

year  = {2022},

date = {2022-01-01},

journal = {IEEE Network},

volume = {36},

issue = {1},

pages = {40-44},

abstract = {Cyber-physical-social systems (CPSS) encompassing the cyber, physical and social worlds and the integrations among them, such as Industrial Internet of Things, has, as one of its main purposes, the provision of intelligent services for humans. For CPSS, the rapid development of smart devices with their increasing computational and storage capabilities makes them an attractive and innovative computing model for edge computing between the CPSS and cloud computing. The intelligent edge services (IES) has revolutionized almost every aspect of daily life. However, to realize highly efficient IES provisions in CPSS, important constraints, such as the execution time with security level, price, and energy consumption, should be considered in edge computing. In addition, the systematic integration of these constraints and the general optimization modeling for edge computing is very challenging. In this article, we propose a comprehensive optimization model for IES provision. To demonstrate the merits of the proposed computing and optimization model, a case study is presented.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A Wearable Tele-Health System towards Monitoring COVID-19 and Chronic Disease},

author = {Wei Jiang and Sumit Majumder and Samarth Kumar and Sophini Subramaniam and Xiaohe Li and Ridha Khedri and Tapas Mondal and Mansour Abolghasemian and Imran Satia and M. Jamal Deen},

doi = {10.1109/RBME.2021.3069815},

year  = {2022},

date = {2022-03-30},

journal = {IEEE Reviews in Biomedical Engineering},

volume = {15},

pages = {61-84},

abstract = {Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic since early 2020. The coronavirus disease 2019 (COVID-19) has already caused more than three million deaths worldwide and affected people's physical and mental health. COVID-19 patients with mild symptoms are generally required to self-isolate and monitor for symptoms at least for 14 days in the case the disease turns towards severe complications. In this work, we overviewed the impact of COVID-19 on the patients' general health with a focus on their cardiovascular, respiratory and mental health, and investigated several existing patient monitoring systems. We addressed the limitations of these systems and proposed a wearable telehealth solution for monitoring a set of physiological parameters that are critical for COVID-19 patients such as body temperature, heart rate, heart rate variability, blood oxygen saturation, respiratory rate, blood pressure, and cough. This physiological information can be further combined to potentially estimate the lung function using artificial intelligence (AI) and sensor fusion techniques. The prototype, which includes the hardware and a smartphone app, showed promising results with performance comparable to or better than similar commercial devices, thus potentially making the proposed system an ideal wearable solution for long-term monitoring of COVID-19 patients and other chronic diseases.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Assessment tools for measurement of dementia-friendliness of a community: A scoping review},

author = {Laura G. Diaz and Evelyne Durocher and Paula Gardner and Carrie McAiney and Vishal Mokashi and Lori Letts},

doi = {10.1177/14713012221090032},

year  = {2022},

date = {2022-07-01},

journal = {Dementia 21},

volume = {21},

issue = {5},

abstract = {Background - A quantitative assessment of the dementia-friendliness of a community can support planning and evaluation of dementia-friendly community (DFC) initiatives, internal review, and national/international comparisons, encouraging a more systematic and strategic approach to the advancement of DFCs. However, assessment of the dementia-friendliness of a community is not always conducted and continuous improvement and evaluation of the impact of dementia-friendly initiatives are not always undertaken. A dearth of applicable evaluation tools is one reason why there is a lack of quantitative assessments of the dementia-friendliness of communities working on DFC initiatives.

Purpose - A scoping review was conducted to identify and examine assessment tools that can be used to conduct quantitative assessments of the dementia-friendliness of a community.

Design and methods - Peer-reviewed studies related to DFCs were identified through a search of seven electronic databases (MEDLINE, CINAHL, PsycINFO, Embase, EMCare, HealthSTAR, and AgeLine). Grey literature on DFCs was identified through a search of the World Wide Web and personal communication with community leads in Australia, Canada, New Zealand, the United Kingdom, and the United States. Characteristics of identified assessment tools were tabulated, and a narrative summary of findings was developed along with a discussion of strengths and weaknesses of identified tools.

Results - Forty tools that assess DFC features (built environment, dementia awareness and attitudes, and community needs) were identified. None of the identified tools were deemed comprehensive enough for the assessment of community needs of people with dementia},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Characterization of Knee and Gait Features from a Wearable Tele-Health Monitoring System},

author = {Abu Ilius Faisal and Tapas Mondal and David Cowan and M. Jamal Deen},

doi = {10.1109/JSEN.2022.3146617},

year  = {2022},

date = {2022-03-15},

journal = {IEEE Sensors Journal},

volume = {22},

issue = {6},

pages = { 4741 - 4753},

abstract = {Mobility is crucial for healthy aging. Any disruption to mobility can affect mental, physical and social health, and socio-economic independence. Therefore, studies in gait and lower-joint functionality with respect to different demographic features will play a vital role in maintaining good mobility. In this study, we analyzed a gait database from 70 healthy subjects (18–86 years) constructed using our custom-built multi-sensor-based wearable tele-health monitoring system. The purpose was to extract and use the most informative features for classifying knee joint and gait characteristics of the subjects with respect to their age, body mass index – BMI, and sex. Four supervised machine learning algorithms: partial least square-discriminant analysis (PLS-DA), support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were used to classify the subjects. The features that significantly contributed to all classifications are knee angle, quadriceps muscle pressure adjacent to the knee joint, rotational energy (mediolateral and vertical), acceleration energy (mediolateral), cross-sample entropy (anteroposterior-mediolateral), knee angle variability, symmetry of swing and stance phase, and walk ratio. Classification accuracies of all four methods were ~89%, 83%, 81%, 86% for age, 90%, 80%, 83%, 86% for BMI, and 97%, 97%, 96%, 97% for sex, respectively. PLS-DA had the best classification performance for all three categories which makes it preferable for these kinds of analyses. Thus, our knee and gait monitoring system coupled with an efficient machine learning tool can be exploited for real-time evaluation and early diagnoses of mobility disabilities, health assessment, and monitoring the need for interventions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator},

author = {Yujiao Hao and Xijian Lou and Boyu Wang and Rong Zheng},

doi = {10.1109/TMC.2022.3230370},

year  = {2022},

date = {2022-12-19},

journal = {IEEE Transactions on Mobile Computing},

pages = {1-12},

abstract = {With the prevalence of wearable devices, inertial measurement unit (IMU) data has been utilized in monitoring and assessing human mobility such as human activity recognition (HAR) and human pose estimation (HPE). Training deep neural network (DNN) models for these tasks require a large amount of labelled data, which are hard to acquire in uncontrolled environments. To mitigate the data scarcity problem, we design CROMOSim, a cross-modality sensor simulator that simulates high fidelity virtual IMU sensor data from motion capture systems or monocular RGB cameras. It utilizes a skinned multi-person linear model (SMPL) for 3D body pose and shape representations to enable simulation from arbitrary on-body positions. Then a DNN model is trained to learn the functional mapping from imperfect trajectory estimations in a 3D SMPL body tri-mesh due to measurement noise, calibration errors, occlusion and other modelling artifacts, to IMU data. We evaluate the fidelity of CROMOSim simulated data and its utility in data augmentation on various HAR and HPE datasets. Extensive empirical results show that the proposed model achieves a 6.7% improvement over baseline methods in a HAR task.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Insole-Based Systems for Health Monitoring: Current Solutions and Research Challenges},

author = {Sophini Subramaniam and Sumit Majumder and Abu Ilius Faisal and M. Jamal Deen},

url = {https://www.mdpi.com/1424-8220/22/2/438#},

year  = {2022},

date = {2022-01-07},

journal = {Sensors},

volume = {22},

issue = {2},

abstract = {Wearable health monitoring devices allow for measuring physiological parameters without restricting individuals’ daily activities, providing information that is reflective of an individual’s health and well-being. However, these systems need to be accurate, power-efficient, unobtrusive and simple to use to enable a reliable, convenient, automatic and ubiquitous means of long-term health monitoring. One such system can be embedded in an insole to obtain physiological data from the plantar aspect of the foot that can be analyzed to gain insight into an individual’s health. This manuscript provides a comprehensive review of insole-based sensor systems that measure a variety of parameters useful for overall health monitoring, with a focus on insole-based PPD measurement systems developed in recent years. Existing solutions are reviewed, and several open issues are presented and discussed. The concept of a fully integrated insole-based health monitoring system and considerations for future work are described. By developing a system that is capable of measuring parameters such as PPD, gait characteristics, foot temperature and heart rate, a holistic understanding of an individual’s health and well-being can be obtained without interrupting day-to-day activities. The proposed device can have a multitude of applications, such as for pathology detection, tracking medical conditions and analyzing gait characteristics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Intrinsically Conductive Liquid Metal-Elastomer Composites for Stretchable and Flexible Electronics},

author = {Kory Schlingman and Gloria M. D'Amaral and R. Stephen Carmichael and Tricia Breen Carmichael},

url = {https://onlinelibrary.wiley.com/doi/10.1002/admt.202200374},

year  = {2022},

date = {2022-07-01},

urldate = {2022-07-01},

journal = {Advanced Materials Technologies},

volume = {8},

issue = {1},

abstract = {Liquid metal-embedded elastomers (LMEEs) are a class of deformable composites made of particles of liquid metal dispersed in an elastomeric matrix. Although these composites possess high thermal conductivity, they are not intrinsically electrically conductive unless a stimulus is applied to join the liquid metal inclusions into a conductive pathway. LMEEs with intrinsic conductivity, especially with a conductive surface, have great potential uses in flexible and stretchable electronics as soft, nondamaging contacts for device characterization, stretchable interconnects for deformable circuits, and as a “soft solder” to electrically connect devices to flexible and stretchable substrates. Here, a simple process is introduced to fabricate intrinsically conductive LMEEs (iLMEEs) with conductive surfaces through the sedimentation of microparticles of eutectic gallium-indium alloy (EGaIn) in the elastomer poly(dimethylsiloxane). During this sedimentation process, an EGaIn-rich 3D percolation network forms at the bottom surface. The resulting iLMEE possesses a conductive surface comprising a mosaic of EGaIn particles embedded in PDMS, with a low sheet resistance of 0.63 ± 0.04 Ω sq–1. iLMEE is soft, stretchable, and exhibits stable conductivity to 100% strain. We demonstrate the use of iLMEE as nondamaging, reusable soft electrical contact probes and as mechanically robust electrical connections between light-emitting devices and flexible plastic substrates.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Invariant Feature Learning for Sensor-based Human Activity Recognition},

author = {Yujiao Hao and Rong Zheng and Boyu Wang},

doi = {10.1109/TMC.2021.3064252},

year  = {2022},

date = {2022-11-01},

journal = {IEEE Transactions on Mobile Computing},

volume = {21},

issue = {11},

pages = {4013 - 4024},

abstract = {Wearable sensor-based human activity recognition (HAR) has been a research focus in the field of ubiquitous and mobile computing for years. In recent years, many deep models have been applied to HAR problems. However, deep learning methods typically require a large amount of data for models to generalize well. Significant variances caused by different participants or diverse sensor devices limit the direct application of a pre-trained model to a subject or device that has not been seen before. To address these problems, we present an invariant feature learning framework (IFLF) that extracts common information shared across subjects and devices. IFLF incorporates two learning paradigms: 1) meta-learning to capture robust features across seen domains and adapt to an unseen one with similarity-based data selection; 2) multi-task learning to deal with data shortage and enhance overall performance via knowledge sharing among different subjects. Experiments demonstrated that IFLF is effective in handling both subject and device diversion across popular open datasets and an in-house dataset. It outperforms a baseline model of up to 40 percent in test accuracy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Validity and reliability of gait metrics derived from researcher-placed and self-placed wearable inertial sensors},

author = {Matthew C. Ruder and Michael A. Hunt and Jesse M. Charlton and Calvin T.F. Tse and Dylan Kobsar },

doi = {10.1016/j.jbiomech.2022.111263},

year  = {2022},

date = {2022-08-18},

volume = {142},

abstract = {To compare the inter-session placement reliability for researcher-placed and self-placed sensors, and to evaluate the validity and reliability of waveforms and discrete variables from researcher-placed and self-placed sensors following a previously described alignment correction algorithm. Fourteen healthy, pain-free participants underwent gait analysis over two data collection sessions. Participants self-placed an inertial sensor on their left tibia and a researcher placed one on their right tibia, before completing 10 overground walking trials. Following an axis correction from a principal component analysis-based algorithm, validity and reliability were assessed within and between days for each sensor placement type through Euclidean distances, waveforms, and discrete outcomes. The placement location of researcher-placed sensors exhibited good inter-session reliability (ICC = 0.85) in comparison to self-placed sensors (ICC = 0.55). Similarly, waveforms from researcher-placed sensors exhibited excellent validity across all variables (CMC ≥ 0.90), while self-placed sensors saw high validity for most axes with reductions in validity for mediolateral acceleration and frontal plane angular velocity. Discrete outcomes saw good to excellent reliability across both sensor placement types. A simple alignment correction algorithm for inertial sensor gait data demonstrated good to excellent validity and reliability in self-placed sensors with no additional data or measures. This method can be used to align sensors easily and effectively despite sensor placement errors during straight, level walking to improve 3D gait data outcomes in data collected with self-placed sensors.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Voice Activated IoT Devices for Healthcare: Design Challenges and Emerging Applications},

author = {Petros Spachos and Stefano Gregori and M. Jamal Deen},

doi = {10.1109/TCSII.2022.3179680},

year  = {2022},

date = {2022-06-01},

journal = {IEEE Transactions on Circuits and Systems II – Express Briefs},

volume = {69},

issue = {7},

pages = {3101 - 3107},

abstract = {The recent pandemic forced substantial changes in our lives, including the way we interact with physical objects. For example, voice-activated systems that enable users to communicate with them through speech commands are becoming more pervasive. At the same time, recent technology developments delivered voice capability to Internet of Things (IoT) devices with low-power audio transducers. Voice-activated IoT devices have the potential to engage patients and caregivers in new and cost-efficient ways, from telehealth and digital health, to portable diagnostics and remotely delivered care. In this brief, we review voice activated IoT devices, discuss their trends, and identify unique challenges when these devices are used in the healthcare sector. Furthermore, we discuss some future application scenarios and their characteristics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Wearable Sensor Systems for Fall Risk Assessment: A Review},

author = {Sophini Subramaniam and Abu Ilius Faisal and M. Jamal Deen},

url = {https://www.frontiersin.org/articles/10.3389/fdgth.2022.921506/full#h1},

year  = {2022},

date = {2022-07-14},

journal = {Frontiers in Digital Health},

volume = {4},

abstract = {Fall risk assessment and fall detection are crucial for the prevention of adverse and long-term health outcomes. Wearable sensor systems have been used to assess fall risk and detect falls while providing additional meaningful information regarding gait characteristics. Commonly used wearable systems for this purpose are inertial measurement units (IMUs), which acquire data from accelerometers and gyroscopes. IMUs can be placed at various locations on the body to acquire motion data that can be further analyzed and interpreted. Insole-based devices are wearable systems that were also developed for fall risk assessment and fall detection. Insole-based systems are placed beneath the sole of the foot and typically obtain plantar pressure distribution data. Fall-related parameters have been investigated using inertial sensor-based and insole-based devices include, but are not limited to, center of pressure trajectory, postural stability, plantar pressure distribution and gait characteristics such as cadence, step length, single/double support ratio and stance/swing phase duration. The acquired data from inertial and insole-based systems can undergo various analysis techniques to provide meaningful information regarding an individual's fall risk or fall status. By assessing the merits and limitations of existing systems, future wearable sensors can be improved to allow for more accurate and convenient fall risk assessment. This article reviews inertial sensor-based and insole-based wearable devices that were developed for applications related to falls. This review identifies key points including spatiotemporal parameters, biomechanical gait parameters, physical activities and data analysis methods pertaining to recently developed systems, current challenges, and future perspectives.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A Simple, Low-cost Multi-sensor-based Smart Wearable Knee Monitoring System},

author = {Abu Ilius Faisal and Sumit Majumder and Ryan Scott and Tapas Mondal and David Cowan and M. Jamal Deen},

doi = {10.1109/JSEN.2020.3044784},

year  = {2021},

date = {2021-03-15},

journal = {IEEE Sensors Journal},

volume = {21},

issue = {6},

pages = {8253 - 8266},

abstract = {Maintaining good mobility with ease and freedom of movement is important for an individual's health and active aging. The knee joint, being the primary bearer of the body weight, plays a vital role in mobility. Continuous monitoring of the knee joint can potentially provide important information related to knee health and mobility which can be used for health assessment, early diagnoses of mobility-related problems, and monitoring recovery from injury or surgery. Therefore, we developed a simple, low-cost multi-sensor-based smart wearable device to monitor and assess the knee joint and mobility. The system is composed of miniaturized sensors (motion, temperature, pressure and galvanic skin response) to measure acceleration, angular velocity, skin temperature, muscle pressure and sweat rate of the knee joint during different activities. A database is constructed from 70 healthy adults aged 18-86 years that contains sensor data measured using the proposed knee joint monitoring system. To extract key knee and gait features from the datasets, we employed computationally efficient methods such as complementary filter and wavelet packet decomposition. The variations in the characteristics of the obtained parameters were analyzed in terms of gender and age groups. This simple, easy-to-use, cost-effective, non-invasive and unobtrusive knee monitoring system can be used for real-time monitoring, evaluation and early diagnoses of joint disorders, fall detection, mobility monitoring and rehabilitation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Natural Brain-Inspired Intelligence for Screening in Healthcare Applications},

author = {Mahdi Naghshvarianjahromi and Sumit Majumder and Shiva Kumar and Narjes Naghshvarianjahromi and M. Jamal Deen},

doi = {10.1109/ACCESS.2021.3077529},

year  = {2021},

date = {2021-05-04},

journal = {IEEE Access},

volume = {9},

pages = {67957 - 67973},

abstract = {In recent years, there has been a growing interest in smart e-Health systems to improve people's quality-of-life by enhancing healthcare accessibility and reducing healthcare costs. Continuous monitoring of health through the smart e-Health system may enable automatic diagnosis of diseases like Arrhythmia at its early onset that otherwise may become fatal if not detected on time. In this work, we developed a cognitive dynamic system (CDS)-based framework for the smart e-Health system to realize an automatic screening process in the presence of a defective or abnormal dataset. A defective dataset may have poor labeling and/or lack enough training patterns. To mitigate the adverse effect of such a defective dataset, we developed a decision-making system that is inspired by the decision-making processes in humans in case of conflict-of-opinions (CoO). We present a proof-of-concept implementation of this framework to automatically identify people having Arrhythmia from single lead Electrocardiogram (ECG) traces. It is shown that the proposed CDS performs well with the diagnosis errors of 13.2%, 9.9%, 6.6%, and 4.6%, being in good agreement with the desired diagnosis errors of 25%, 10%, 5.9%, and 2.5%, respectively. The proposed CDS algorithm can be incorporated in the autonomic computing layer of a smart-e-Health-home platform to achieve a pre-defined degree of screening accuracy in the presence of a defective dataset.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Wearable IMU-based System for Real-time Monitoring of Lower-limb Joints},

author = {Sumit Majumder and M. Jamal Deen },

doi = {10.1109/JSEN.2020.3044800},

year  = {2021},

date = {2021-03-15},

journal = {IEEE Sensors Journal},

volume = {21},

issue = {6},

pages = {8267 - 8275},

abstract = {Recent advances in micro-electromechanical systems technology have enabled the evolution of miniature, low-power, and high-performance inertial motion sensors that are commonly found in most present-day smart gadgets. Furthermore, high-speed and power-efficient communication and computing technologies may enable these sensors to potentially pave the way for home-based remote monitoring and assessment of human health in the imminent age of new technologies such as Smart Home, internet-of-things, and internet-of-everything. Continuous monitoring of lower-limb joints in a wearable platform is such an application that may potentially enable the tele-rehabilitation of patients with motor impairment, gait abnormalities, and joint injuries through quantitative rather than observational analysis of gait health. In this work, we designed, implemented, and validated a two-stage sensor fusion algorithm to estimate lower-limb joint angles in real-time. The drift in the cumulatively integrated gyroscope data was estimated in real-time using a gradient descent approach that was subsequently used to correct the inclination of the sensors. The roll and pitch angles thus obtained for each sensor mounted above and below the joint were then fused in the second stage to obtain a real-time estimate of joint angle by exploiting a gradient descent method. Since the joint angles were estimated primarily from the gyroscope data and without incorporating any magnetic field measurement, the joint angles thus obtained were least affected by the external acceleration and are insensitive to magnetic disturbances. The performance of the proposed algorithm was validated with a publicly available dataset and in the presence of simulated external acceleration.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A Robust Orientation Filter for Wearable Sensing Applications},

author = {Sumit Majumder and M. Jamal Deen },

doi = {10.1109/JSEN.2020.3009388},

year  = {2020},

date = {2020-12-01},

journal = {IEEE Sensors Journal},

volume = {20},

issue = {23},

pages = {14228 - 14236},

abstract = {Advancements in the micro-electromechanical systems technology have enabled the realization of small-size, high-performance inertial motion and magnetic field sensors that are embedded in most modern-day smart gadgets. These sensors, when coupled with the high-speed computing and communication technologies may potentially enable in-home monitoring and assessment of human health in the forthcoming age of Smart home technologies, internet-of-thing, and internet-of-everything. However, because the sensor's orientation is generally arbitrary, this may cause erroneous results of important health parameters such as gait speed and range of motion of the knee joint. Therefore, it is important that the sensor's measurements be corrected for orientation. In this work, we designed, implemented, and validated a three-stage sensor fusion algorithm. A gradient descent approach was exploited to estimate the drift in and subtract it from the cumulatively integrated gyroscope data to obtain the orientation in real time. The roll and pitch angles were obtained from the first stage, whereas the second and third stages outputs a coarse and fine estimate of yaw angle, respectively. Since the estimation was obtained primarily from the gyroscope data, the estimated orientation was least affected by the external acceleration and magnetic disturbances. The performance of the proposed algorithm was validated with a publicly available dataset, and in presence of external acceleration and magnetic disturbances. Finally, some key gait parameters were derived from the gait measurements using the proposed filter that showed high conformity to the ground-truth values.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Homecare Robotic Systems for Healthcare 4.0: Visions and Enabling Technologies},

author = {Geng Yang and Zhibo Pang and M. Jamal Deen and Mianxiong Dong and Yuan-Ting Zhang and Nigel Lovell and Amir M. Rahmani},

doi = {10.1109/JBHI.2020.2990529},

year  = {2020},

date = {2020-09-01},

journal = {IEEE Journal of Biomedical and Health Informatics},

volume = {24},

issue = {9},

pages = {2535 - 2549},

abstract = {Powered by the technologies that have originated from manufacturing, the fourth revolution of healthcare technologies is happening (Healthcare 4.0). As an example of such revolution, new generation homecare robotic systems (HRS) based on the cyber-physical systems (CPS) with higher speed and more intelligent execution are emerging. In this article, the new visions and features of the CPS-based HRS are proposed. The latest progress in related enabling technologies is reviewed, including artificial intelligence, sensing fundamentals, materials and machines, cloud computing and communication, as well as motion capture and mapping. Finally, the future perspectives of the CPS-based HRS and the technical challenges faced in each technical area are discussed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gardner2020,

title = {Introduction: Emerging Gender, Media and Technology Scholarship in Africa: Opportunities and Conundrums in African Women’s Navigating Digital Media},

author = {Paula Gardner and Audrey Gadzekpo and H.Leslie Steeves},

url = {https://fembotcollective.manifoldapp.org/read/ada-16-043fcc85-89f5-49c9-b77f-76e03a2b79a9/section/128bfea5-a7e1-42a5-bf24-4b09dae40835},

year  = {2020},

date = {2020-01-01},

urldate = {2020-09-13},

journal = {Ada: A Journal of Gender, New Media, and Technology},

number = {16},

publisher = {Fembot Collective},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Wearable inertial sensors for gait analysis in adults with osteoarthritis – A scoping review},

author = {Dylan Kobsar and Zaryan Masood and Heba Khan and Noha Khalil and Marium Yossri Kiwan and Sarah Ridd and Mathew Tobis},

url = {https://www.mdpi.com/1424-8220/20/24/7143},

year  = {2020},

date = {2020-12-13},

journal = {Sensors},

volume = {20},

issue = {24},

abstract = {Our objective was to conduct a scoping review which summarizes the growing body of literature using wearable inertial sensors for gait analysis in lower limb osteoarthritis. We searched six databases using predetermined search terms which highlighted the broad areas of inertial sensors, gait, and osteoarthritis. Two authors independently conducted title and abstract reviews, followed by two authors independently completing full-text screenings. Study quality was also assessed by two independent raters and data were extracted by one reviewer in areas such as study design, osteoarthritis sample, protocols, and inertial sensor outcomes. A total of 72 articles were included, which studied the gait of 2159 adults with osteoarthritis (OA) using inertial sensors. The most common location of OA studied was the knee (n = 46), followed by the hip (n = 22), and the ankle (n = 7). The back (n = 41) and the shank (n = 40) were the most common placements for inertial sensors. The three most prevalent biomechanical outcomes studied were: mean spatiotemporal parameters (n = 45), segment or joint angles (n = 33), and linear acceleration magnitudes (n = 22). Our findings demonstrate exceptional growth in this field in the last 5 years. Nevertheless, there remains a need for more longitudinal study designs, patient-specific models, free-living assessments, and a push for “Code Reuse” to maximize the unique capabilities of these devices and ultimately improve how we diagnose and treat this debilitating disease.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}