FIT A Fog Computing Device for Speech TeleTreatments

There is an increasing demand for smart fogcomputing gateways as the size of cloud data is growing. This paper presents a Fog computing interface (FIT) for processing clinical speech data. FIT builds upon our previous work on EchoWear, a wearable technology that validated the use of smartwatches for collecting clinical speech data from patients with Parkinson's disease (PD). The fog interface is a low-power embedded system that acts as a smart interface between the smartwatch and the cloud. It collects, stores, and processes the speech data before sending speech features to secure cloud storage. We developed and validated a working prototype of FIT that enabled remote processing of clinical speech data to get speech clinical features such as loudness, short-time energy, zero-crossing rate, and spectral centroid. We used speech data from six patients with PD in their homes for validating FIT. Our results showed the efficacy of FIT as a Fog interface to translate the clinical speech processing chain (CLIP) from a cloud-based backend to a fog-based smart gateway.Comment: 3 pages, 5 figures, 1 table, 2nd IEEE International Conference on Smart Computing SMARTCOMP 2016, Missouri, USA, 201

A Multi-Smartwatch System for Assessing Speech Characteristics of People with Dysarthria in Group Settings

Speech-language pathologists (SLPs) frequently use vocal exercises in the treatment of patients with speech disorders. Patients receive treatment in a clinical setting and need to practice outside of the clinical setting to generalize speech goals to functional communication. In this paper, we describe the development of technology that captures mixed speech signals in a group setting and allows the SLP to analyze the speech signals relative to treatment goals. The mixed speech signals are blindly separated into individual signals that are preprocessed before computation of loudness, pitch, shimmer, jitter, semitone standard deviation and sharpness. The proposed method has been previously validated on data obtained from clinical trials of people with Parkinson disease and healthy controls.Comment: 6 page, 9 figure, 1 table, 8 equations, Proceedings e-Health Networking, Applications and Services (Healthcom), 2015 IEEE 17th International Conference on, Boston, USA. 201

Smart fog: Fog computing framework for unsupervised clustering analytics in wearable Internet of Things

The increasing use of wearables in smart telehealth system led to the generation of large medical big data. Cloud and fog services leverage these data for assisting clinical procedures. IoT Healthcare has been benefited from this large pool of generated data. This paper suggests the use of low-resource machine learning on Fog devices kept close to wearables for smart telehealth. For traditional telecare systems, the signal processing and machine learning modules are deployed in the cloud that processes physiological data. This paper presents a Fog architecture that relied on unsupervised machine learning big data analysis for discovering patterns in physiological data. We developed a prototype using Intel Edison and Raspberry Pi that was tested on real-world pathological speech data from telemonitoring of patients with Parkinson\u27s disease (PD). Proposed architecture employed machine learning for analysis of pathological speech data obtained from smart watches worn by the patients with PD. Results show that proposed architecture is promising for low-resource machine learning. It could be useful for other applications within wearable IoT for smart telehealth scenarios by translating machine learning approaches from the cloud backend to edge computing devices such as Fog

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

EchoWear: Smartwatch Technology for Voice and Speech Treatments of Patients with Parkinson’s Disease

About 90 percent of people with Parkinson\u27s disease (PD) experience decreased functional communication due to the presence of voice and speech disorders associated with dysarthria that can be characterized by monotony of pitch (or fundamental frequency), reduced loudness, irregular rate of speech, imprecise consonants, and changes in voice quality. Speech-language pathologists (SLPs) work with patients with PD to improve speech intelligibility using various intensive in-clinic speech treatments. SLPs also prescribe home exercises to enhance generalization of speech strategies outside of the treatment room. Even though speech therapies are found to be highly effective in improving vocal loudness and speech quality, patients with PD find it difficult to follow the prescribed exercise regimes outside the clinic and to continue exercises once the treatment is completed. SLPs need techniques to monitor compliance and accuracy of their patients\u27 exercises at home and in ecologically valid communication situations. We have designed EchoWear, a smartwatch-based system, to remotely monitor speech and voice exercises as prescribed by SLPs. We conducted a study of 6 individuals; three with PD and three healthy controls. To assess the performance of EchoWear technology compared with high-quality audio equipment obtained in a speech laboratory. Our preliminary analysis shows promising outcomes for using EchoWear in speech therapies for people with PD

SmartEAR: Smartwatch-based Unsupervised Learning for Multi-modal Signal Analysis in Opportunistic Sensing Framework

Wrist-bands such as smartwatches have become an unobtrusive interface for collecting physiological and contextual data from users. Smartwatches are being used for smart healthcare, telecare, and wellness monitoring. In this paper, we used data collected from the AnEAR framework leveraging smartwatches to gather and store physiological data from patients in naturalistic settings. This data included temperature, galvanic skin response (GSR), acceleration, and heart rate (HR). In particular, we focused on HR and acceleration, as these two modalities are often correlated. Since the data was unlabeled we relied on unsupervised learning for multi-modal signal analysis. We propose using k-means clustering, GMM clustering, and Self-Organizing maps based on Neural Networks for group the multi-modal data into homogeneous clusters. This strategy helped in discovering latent structures in our data

Acceptance and perception of digital health for managing nutrition in people with Parkinson\u27s disease and their caregivers and their digital competence in the United States: A mixed-methods study

Background and aims: This mixed-methods study examined participants\u27 acceptance and perception of using digital health for managing nutrition and participants\u27 digital competence. The results will be formative for making digital nutrition education more effective and acceptable for people with Parkinson\u27s disease (PwPD) and their informal caregivers. Methods: Qualitative data were collected through in-person semi-structured, dyadic interviews, and questionnaires from 20 dyads (20 PwPD and their caregivers) in the Northeastern United States and analyzed throughout the 2018 to 2019 academic year. Interview transcripts were deductively coded using the framework analysis method. Phrases related to acceptance of digital health were sub-coded into accept, neutral, or reject and those related to perceptions of digital health were sub-coded into perceived usefulness, perceived ease of use, and awareness of digital health. Quantitative data were analyzed using independent samples t tests and Fisher\u27s exact tests. Qualitative codes were transformed into variables and compared to digital competence scores to integrate the data. An average acceptance rate for digital health was calculated through examining the mean percent of phrases coded as accept from interview transcripts. Results: Twenty-five of 40 (62.5%) participants used the internet for at least 5 health-related purposes and the average acceptance rate was 54.4%. Dyads rejected digital health devices if they did not see the added benefit. The majority of participants reported digital health to be useful, but hard to use, and about half felt they needed education about existing digital health platforms. There was no difference in digital competence scores between PwPD and their caregivers (28.6 ± 12.6). Conclusion: Findings suggest that dyads accept and use technology but not to its full potential as technology can be perceived as hard to use. This finding, combined with digital competence scores, revealed that education is warranted prior to providing a digital nutrition intervention

Impact on Diet Quality and Resilience in Urban Community Dwelling Obese Women with a Nutrition and Physical Activity Intervention

Objective: To examine the effect of a Tai Chi, resistance training, and behaviorally-based diet education intervention on dietary quality as well as resilience and physical resilience in obese older women. Design: Community health outreach with a quasi-experimental design. Setting: An urban senior center in Rhode Island. Participants: Thirty-three women, 85% were minorities, with mean age of 65±8.2 years and BMI of 37.3±4.6 kg/m2, were enrolled in the study at baseline however only 17 women in the intervention (EXD) group and 9 women in the wait-list control (CON) group completed the study. Measurement: Dietary quality and nutrition risk were measured using the Dietary Screening Tool (DST), resilience was measured by the Resilience Scale, and physical resilience was examined using the Physical Resilience Scale. Intervention: Participants in the EXD group engaged in 12 weeks of Tai Chi, resistance training, and behaviorally-based diet education. The diet education was based off of the modified Dietary Approaches to Stop Hypertension (DASH) diet and led by a Registered Dietitian. Results: There was no change in dietary quality by group or time. However the EXD group had significantly higher dietary quality compared to the control group (p=0.025) at post intervention, although there was no difference in nutrition risk category. There was no change seen in overall resilience, however the EXD group improved physical resilience (p=0.048). Conclusion: A community health outreach that involved Tai Chi, resistance training, and behaviorally-based diet education may promote higher dietary quality as well as improve physical resilience in obese older women

The Long-Baseline Neutrino Experiment: Exploring Fundamental Symmetries of the Universe

The preponderance of matter over antimatter in the early Universe, the dynamics of the supernova bursts that produced the heavy elements necessary for life and whether protons eventually decay --- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our Universe, its current state and its eventual fate. The Long-Baseline Neutrino Experiment (LBNE) represents an extensively developed plan for a world-class experiment dedicated to addressing these questions. LBNE is conceived around three central components: (1) a new, high-intensity neutrino source generated from a megawatt-class proton accelerator at Fermi National Accelerator Laboratory, (2) a near neutrino detector just downstream of the source, and (3) a massive liquid argon time-projection chamber deployed as a far detector deep underground at the Sanford Underground Research Facility. This facility, located at the site of the former Homestake Mine in Lead, South Dakota, is approximately 1,300 km from the neutrino source at Fermilab -- a distance (baseline) that delivers optimal sensitivity to neutrino charge-parity symmetry violation and mass ordering effects. This ambitious yet cost-effective design incorporates scalability and flexibility and can accommodate a variety of upgrades and contributions. With its exceptional combination of experimental configuration, technical capabilities, and potential for transformative discoveries, LBNE promises to be a vital facility for the field of particle physics worldwide, providing physicists from around the globe with opportunities to collaborate in a twenty to thirty year program of exciting science. In this document we provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess.Comment: Major update of previous version. This is the reference document for LBNE science program and current status. Chapters 1, 3, and 9 provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess. 288 pages, 116 figure