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BackgroundComprehensive exams such as the Dean-Woodcock Neuropsychological Assessment System, the Global Deterioration Scale, and the Boston Diagnostic Aphasia Examination are the gold standard for doctors and clinicians in the preliminary assessment and monitoring of neurocognitive function in conditions such as neurodegenerative diseases and acquired brain injuries (ABIs). In recent years, there has been an increased focus on implementing these exams on mobile devices to benefit from their configurable built-in sensors, in addition to scoring, interpretation, and storage capabilities. As smartphones become more accepted in health care among both users and clinicians, the ability to use device information (eg, device position, screen interactions, and app usage) for subject monitoring also increases. Sensor-based assessments (eg, functional gait using a mobile device’s accelerometer and/or gyroscope or collection of speech samples using recordings from the device’s microphone) include the potential for enhanced information for diagnoses of neurological conditions; mapping the development of these conditions over time; and monitoring efficient, evidence-based rehabilitation programs. ObjectiveThis paper provides an overview of neurocognitive conditions and relevant functions of interest, analysis of recent results using smartphone and/or tablet built-in sensor information for the assessment of these different neurocognitive conditions, and how human-device interactions and the assessment and monitoring of these neurocognitive functions can be enhanced for both the patient and health care provider. MethodsThis survey presents a review of current mobile technological capabilities to enhance the assessment of various neurocognitive conditions, including both neurodegenerative diseases and ABIs. It explores how device features can be configured for assessments as well as the enhanced capability and data monitoring that will arise due to the addition of these features. It also recognizes the challenges that will be apparent with the transfer of these current assessments to mobile devices. ResultsBuilt-in sensor information on mobile devices is found to provide information that can enhance neurocognitive assessment and monitoring across all functional categories. Configurations of positional sensors (eg, accelerometer, gyroscope, and GPS), media sensors (eg, microphone and camera), inherent sensors (eg, device timer), and participatory user-device interactions (eg, screen interactions, metadata input, app usage, and device lock and unlock) are all helpful for assessing these functions for the purposes of training, monitoring, diagnosis, or rehabilitation. ConclusionsThis survey discusses some of the many opportunities and challenges of implementing configured built-in sensors on mobile devices to enhance assessments and monitoring of neurocognitive functions as well as disease progression across neurodegenerative and acquired neurological conditions.

Background: Mobile devices and their capabilities (e.g., device sensors and human-device interactions) are increasingly being considered for use in clinical assessments and disease monitoring due to their ability to provide objective, repeatable, and more accurate measures of neurocognitive performance. These mobile-based assessments also provide a foundation for the design of intervention recommendations. Objective: The purpose of this work was to assess the benefits of various physical intervention programs as they relate to Parkinson's disease (PD), its symptoms, and stages (Hoehn and Yahr (H&Y) Stages 1–5). Methods: Ninety-five participants (n = 70 PD; n = 25 control) completed 14 tablet-based neurocognitive functional tests (e.g., motor, memory, speech, executive, and multi-function) and standardized health questionnaires. 208 symptom-specific digital features were normalized to assess the benefits of various physical intervention programs (e.g., aerobic activity, non-contact boxing, functional strength, and yoga) for individuals with PD. While previous studies have shown that physical interventions improve both motor and non-motor PD symptoms, this paper expands on previous works by mapping symptom-specific neurocognitive functionalities to specific physical intervention programs across stages of PD. Results: For early-stage PD (e.g., H&Y Stages 1 & 2), functional strength activities provided the largest overall significant delta improvement (Δ= 0.1883; p = 0.0265), whereas aerobic activity provided the largest overall significant delta improvement (Δ= 0.2700; p = 0.0364) for advanced stages of PD (e.g., H&Y Stages 3–5). Conclusions: As mobile-based digital health technology allows for the collection of larger, labeled, objective datasets, new ways to analyze and interpret patterns in this data emerge which can ultimately lead to new personalized medicine programs. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.compeleceng.2009.03.009 Byline: Jun Yi, Christian Poellabauer Keywords: Multicast; Wireless multihop routing; Medium access control; Priority scheduling; Real-time systems Abstract: Wireless multicast for multihop ad-hoc networks ensures that the same information is delivered efficiently to multiple recipients simultaneously. However, existing protocols ignore the real-time nature of many wireless applications such as real-time video and audio communications in mesh and ad-hoc networks. Further, devices in such networks are typically energy-constrained, i.e., multicast protocols have to trade off timeliness with energy efficiency. This paper presents RTMC, a real-time multicast protocol for wireless multihop networks that allows receivers to specify their individual real-time constraints and establishes multicast trees such that receivers' deadlines are met and overall energy consumption in the network is reduced. Toward this end, RTMC relies on dynamically prioritized packet scheduling on forwarding nodes, prioritized channel access at the MAC layer, feedback control for latency management, tree adaptations to address mobility, dynamic subscriptions and unsubscriptions, and adaptations to address link breakages. Simulation results show that, compared to non-real-time multicast techniques, RTMC can provide user-specific latency management while reducing the energy consumption in the network. Author Affiliation: Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States Article Note: (footnote) [star] This work has been made possible by NSF Grant 0545899.

Background: Due to the COVID-19 pandemic, beneficial physical intervention classes for individuals with Parkinson's disease (PD) were cancelled. Objective: To understand effects of the COVID-19 stay-at-home mandate and the inability to participate in recommended and structured physical interventions as a consequence of these mandates, specifically designed mobile assessments were used that collected both self-reporting information and objective task-based metrics of neurocognitive functions to assess symptom changes for individuals with PD. Methods: Self-reporting questionnaires focusing on overall quality of life (e.g., when individuals typically feel at their best, changes in activity levels, and symptom progression) were given to all individuals (n = 28). In addition, mobile-based neurocognitive assessments were administered to a subset of the population (n = 8) to quantitatively assess changes due to COVID-19 restrictions. Results: The highest self-reported factors in which individuals denoted feeling their best were after exercise (67.86%) and being in a comfortable and supportive environment (60.71%). Objective measures found overall duration of physical activity during the stay-at-home mandate decreased significantly (p = 0.022). With the lack of overall activity, 82.14%of individuals self-reported having at least one symptom that worsened moderately or higher. Further testing, using mobile-based assessments, showed average completion times of functional tasks increased, taking about 2.1 times longer, while accuracy metrics showed overall degradation. Conclusion: Although the COVID-19 stay-at-home mandate was intended to help protect individuals at high risk from coming into contact with the virus, it also prevented individuals from receiving recommended supervised exercise interventions resulting in significant negative effects in social well-being and across motor and speech neurocognitive tasks for individuals with PD. [ABSTRACT FROM AUTHOR]

Depression and anxiety are critical public health issues affecting millions of people around the world. To identify individuals who are vulnerable to depression and anxiety, predictive models have been built that typically utilize data from one source. Unlike these traditional models, in this study, we leverage a rich heterogeneous dataset from the University of Notre Dame's NetHealth study that collected individuals' (student participants') social interaction data via smartphones, health-related behavioral data via wearables (Fitbit), and trait data from surveys. To integrate the different types of information, we model the NetHealth data as a heterogeneous information network (HIN). Then, we redefine the problem of predicting individuals' mental health conditions (depression or anxiety) in a novel manner, as applying to our HIN a popular paradigm of a recommender system (RS), which is typically used to predict the preference that a person would give to an item (e.g., a movie or book). In our case, the items are the individuals' different mental health states. We evaluate four state-of-the-art RS approaches. Also, we model the prediction of individuals' mental health as another problem type--that of node classification (NC) in our HIN, evaluating in the process four node features under logistic regression as a proof-of-concept classifier. We find that our RS and NC network methods produce more accurate predictions than a logistic regression model using the same NetHealth data in the traditional non-network fashion as well as a random-approach. Also, we find that the best of the considered RS approaches outperforms all considered NC approaches. This is the first study to integrate smartphone, wearable sensor, and survey data in a HIN manner and use RS or NC on the HIN to predict individuals' mental health conditions. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is increasingly empowering people with an interconnected world of physical objects ranging from smart buildings to portable smart devices, such as wearables. With recent advances in mobile sensing, wearables have become a rich collection of portable sensors and are able to provide various types of services, including tracking of health and fitness, making financial transactions, and unlocking smart locks and vehicles. Most of these services are delivered based on users’ confidential and personal data, which are stored on these wearables. Existing explicit authentication approaches (i.e., PINs or pattern locks) for wearables suffer from several limitations, including small or no displays, risk of shoulder surfing, and users’ recall burden. Oftentimes, users completely disable security features out of convenience. Therefore, there is a need for a burden-free (implicit) authentication mechanism for wearable device users based on easily obtainable biometric data. In this paper, we present an implicit wearable device user authentication mechanism using combinations of three types of coarse-grain minute-level biometrics: behavioral (step counts), physiological (heart rate), and hybrid (calorie burn and metabolic equivalent of task). From our analysis of over 400 Fitbit users from a 17-month long health study, we are able to authenticate subjects with average accuracy values of around.93 (sedentary) and.90 (non-sedentary) with equal error rates of.05 using binary SVM classifiers. Our findings also show that the hybrid biometrics perform better than other biometrics and behavioral biometrics do not have a significant impact, even during non-sedentary periods. [ABSTRACT FROM AUTHOR]

Mental health (MH) has become a global issue. Digital phenotyping in mental healthcare provides a highly effective, scaled, cost-effective approach to handling global MH problems. We propose an MH monitoring application. The application monitors overall MH based on mood, stress, behavior, and personality. Further, it proposes objective MH assessment from smartphone data and subjective screening of MH via periodic, short, self-report standardized questionnaires. [ABSTRACT FROM AUTHOR]

The achievable accuracy of localization based on received signal strength measurements depends on the path loss exponent (PLE), which describes the multipath fading and shadowing characteristics of a channel. An inaccurate estimation of the PLE leads to inaccurate estimation of positions. In this letter, we introduce a new ranging technique, called VariLoc, which uses a multi-range beaconing approach to perform localization and PLE estimation. The main novelty of VariLoc is that it leverages the sensitivity level of a receiver device for sensing the channel and the transmission power, instead of the more commonly used RSSI measurements, which can be very noisy. This letter shows that VariLoc is robust against multipath fading and can estimate the PLE accurately, even in the presence of very deep fading. This letter further introduces a centroid localization algorithm that utilizes VariLoc to obtain estimates for both the PLE and the position of a target node. The performance of the proposed approach is compared with the state-of-the-art localization algorithms and Cramer–Rao bound using simulation and real-world experimentation using Bluetooth beacons. [ABSTRACT FROM AUTHOR]

It is becoming increasingly important to accurately detect a user's presence at certain locations during certain times of the day, e.g., to study the user's patterns with respect to mobility, behavior, or social interactions and to enable the delivery of targeted services. However, instead of geographic locations, it is often more important to determine a locale that is relevant to the user, e.g., the place of work, home, homes of family and friends, social gathering places, etc. These significant personal places can be determined through analysis, e.g., via segmentation of location traces into a discrete sequence of places. However, segmentation of traces with many gaps (e.g., due to loss of network connectivity or GPS signal) results in a large number of small segments, where many of these segments actually belong together. This work proposes a novel segmentation approach that opportunistically fills gaps in a user's location trace by borrowing location data from other co-located users utilizing the power of mobile crowd sensing and computing (MCSC) paradigm. Through our analysis of four separate large-scale crowd sensing study datasets, we show that our approach yields more and larger segments than the state-of-the-art, where each segment accurately represents the presence of a user at a significant personal place. [ABSTRACT FROM AUTHOR]

This paper shows that extraction and analysis of various acoustic features from speech using mobile devices can allow the detection of patterns that could be indicative of neurological trauma. This may pave the way for new types of biomarkers and diagnostic tools. Toward this end, we created a mobile application designed to diagnose mild traumatic brain injuries (mTBI) such as concussions. Using this application, data were collected from youth athletes from 47 high schools and colleges in the Midwestern United States. In this paper, we focus on the design of a methodology to collect speech data, the extraction of various temporal and frequency metrics from that data, and the statistical analysis of these metrics to find patterns that are indicative of a concussion. Our results suggest a strong correlation between certain temporal and frequency features and the likelihood of a concussion. [ABSTRACT FROM AUTHOR]

Vehicle-to-vehicle safety communications based on the dedicated short-range communication technology have the potential to enable a set of applications that help avoid traffic accidents. The performance of these applications, largely affected by the reliability of communication links, stringently ties back to the MAC and PHY layer design, which has been standardized as IEEE 802.11p. The link reliabilities depend on the signal-to-interference-plus-noise ratio (SINR), which, in turn, depends on the locations and transmit power values of the transmitting nodes. Hence, an accurate network model needs to take into account the network geometry. For such geometric models, however, there is a lack of mathematical understanding of the characteristics and performance of IEEE 802.11p. Important questions such as the scalability performance of IEEE 802.11p have to be answered by simulations, which can be very time consuming and provide limited insights to future protocol design. In this paper, we investigate the performance of IEEE 802.11p by proposing a novel mathematical model based on queuing theory and stochastic geometry. In particular, we extend the Matérn hard-core type-II process with a discrete and nonuniform distribution, which is used to derive the temporal states of backoff counters. By doing so, concurrent transmissions from nodes within the carrier sensing ranges of each other are taken into account, leading to a more accurate approximation to real network dynamics. A comparison with Network Simulator 2 (ns2) simulations shows that our model achieves a good approximation in networks with different densities. [ABSTRACT FROM PUBLISHER]

Proximity-awareness, that is, a mobile device being aware of the presence and capabilities of other devices in its proximity, can be beneficial in many frameworks that support spontaneous sharing of sensors available in advanced personal mobile devices (e.g., smartphones and tablets). Providing Quality of Service (QoS) guarantees in such frameworks is highly challenging because of the dynamic and loosely coupled nature of Mobile Ad Hoc Networks (MANETs). A framework called the SPontaneous Information and Resource sharing InfrasTructure (SPIRIT) has been presented before to address the challenges of proximity-aware mobile sensor sharing. SPIRIT allows mobile applications to share sensors as services in an automatic fashion by enabling the service providers and clients to express a variety of QoS specifications. This paper presents a set of mobility-aware QoS mechanisms for enabling the implementation of QoS specifications along themultihop paths between service providers and clients in highlymobile environments. Simulations in small-scale Mobile Ad Hoc Networks show SPIRIT's ability to effectively control and manage traffic flows while maintaining desired QoS. The implications of the proposed QoS mechanisms extend beyond the scope of SPIRIT, as QoS provisioning is an important issue in many MANET frameworks and applications including Vehicular Ad Hoc Networks (VANETs). [ABSTRACT FROM AUTHOR]