Acetylcholine neuromodulation in normal and abnormal learning and memory: vigilance control in waking, sleep, autism, amnesia, and Alzheimer's disease

This article provides a unified mechanistic neural explanation of how learning, recognition, and cognition break down during Alzheimer's disease, medial temporal amnesia, and autism. It also clarifies whey there are often sleep disturbances during these disorders. A key mechanism is how acetylcholine modules vigilance control in cortical layer

Improving activity recognition using a wearable barometric pressure sensor in mobility-impaired stroke patients.

© 2015 Massé et al.Background: Stroke survivors often suffer from mobility deficits. Current clinical evaluation methods, including questionnaires and motor function tests, cannot provide an objective measure of the patients mobility in daily life. Physical activity performance in daily-life can be assessed using unobtrusive monitoring, for example with a single sensor module fixed on the trunk. Existing approaches based on inertial sensors have limited performance, particularly in detecting transitions between different activities and postures, due to the inherent inter-patient variability of kinematic patterns. To overcome these limitations, one possibility is to use additional information from a barometric pressure (BP) sensor. Methods: Our study aims at integrating BP and inertial sensor data into an activity classifier in order to improve the activity (sitting, standing, walking, lying) recognition and the corresponding body elevation (during climbing stairs or when taking an elevator). Taking into account the trunk elevation changes during postural transitions (sit-to-stand, stand-to-sit), we devised an event-driven activity classifier based on fuzzy-logic. Data were acquired from 12 stroke patients with impaired mobility, using a trunk-worn inertial and BP sensor. Events, including walking and lying periods and potential postural transitions, were first extracted. These events were then fed into a double-stage hierarchical Fuzzy Inference System (H-FIS). The first stage processed the events to infer activities and the second stage improved activity recognition by applying behavioral constraints. Finally, the body elevation was estimated using a pattern-enhancing algorithm applied on BP. The patients were videotaped for reference. The performance of the algorithm was estimated using the Correct Classification Rate (CCR) and F-score. The BP-based classification approach was benchmarked against a previously-published fuzzy-logic classifier (FIS-IMU) and a conventional epoch-based classifier (EPOCH). Results: The algorithm performance for posture/activity detection, in terms of CCR was 90.4 %, with 3.3 % and 5.6 % improvements against FIS-IMU and EPOCH, respectively. The proposed classifier essentially benefits from a better recognition of standing activity (70.3 % versus 61.5 % [FIS-IMU] and 42.5 % [EPOCH]) with 98.2 % CCR for body elevation estimation. Conclusion: The monitoring and recognition of daily activities in mobility-impaired stoke patients can be significantly improved using a trunk-fixed sensor that integrates BP, inertial sensors, and an event-based activity classifier

High Level Learning Using the Temporal Features of Human Demonstrated Sequential Tasks

Modelling human-led demonstrations of high-level sequential tasks is fundamental to a number of practical inference applications including vision-based policy learning and activity recognition. Demonstrations of these tasks are captured as videos with long durations and similar spatial contents. Learning from this data is challenging since inference cannot be conducted solely on spatial feature presence and must instead consider how spatial features play out across time. To be successful these temporal representations must generalize to variations in the duration of activities and be able to capture relationships between events expressed across the scale of an entire video. Contemporary deep learning architectures that represent time (convolution-based and Recurrent Neural Networks) do not address these concerns. Representations learned by these models describe temporal features in terms of fixed durations such as minutes, seconds, and frames. They are also developed sequentially and must use unreasonably large models to capture temporal features expressed at scale. Probabilistic temporal models have been successful in representing the temporal information of videos in a duration invariant manner that is robust to scale, however, this has only been accomplished through the use of user-defined spatial features. Such abstractions make unrealistic assumptions about the content being expressed in these videos, the quality of the perception model, and they also limit the potential applications of trained models. To that end, I present D-ITR-L, a temporal wrapper that extends the spatial features extracted from a typically CNN architecture and transforms them into temporal features. D-ITR-L-derived temporal features are duration invariant and can identify temporal relationships between events at the scale of a full video. Validation of this claim is conducted through various vision-based policy learning and action recognition settings. Additionally, these studies show that challenging visual domains such as human-led demonstration of high-level sequential tasks can be effectively represented when using a D-ITR-L-based model

High Level Learning Using the Temporal Features of Human Demonstrated Sequential Tasks

Modelling human-led demonstrations of high-level sequential tasks is fundamental to a number of practical inference applications including vision-based policy learning and activity recognition. Demonstrations of these tasks are captured as videos with long durations and similar spatial contents. Learning from this data is challenging since inference cannot be conducted solely on spatial feature presence and must instead consider how spatial features play out across time. To be successful these temporal representations must generalize to variations in the duration of activities and be able to capture relationships between events expressed across the scale of an entire video. Contemporary deep learning architectures that represent time (convolution-based and Recurrent Neural Networks) do not address these concerns. Representations learned by these models describe temporal features in terms of fixed durations such as minutes, seconds, and frames. They are also developed sequentially and must use unreasonably large models to capture temporal features expressed at scale. Probabilistic temporal models have been successful in representing the temporal information of videos in a duration invariant manner that is robust to scale, however, this has only been accomplished through the use of user-defined spatial features. Such abstractions make unrealistic assumptions about the content being expressed in these videos, the quality of the perception model, and they also limit the potential applications of trained models. To that end, I present D-ITR-L, a temporal wrapper that extends the spatial features extracted from a typically CNN architecture and transforms them into temporal features. D-ITR-L-derived temporal features are duration invariant and can identify temporal relationships between events at the scale of a full video. Validation of this claim is conducted through various vision-based policy learning and action recognition settings. Additionally, these studies show that challenging visual domains such as human-led demonstration of high-level sequential tasks can be effectively represented when using a D-ITR-L-based model

Integrating Symbolic and Neural Processing in a Self-Organizing Architechture for Pattern Recognition and Prediction

British Petroleum (89A-1204); Defense Advanced Research Projects Agency (N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225

Multisensory integration across exteroceptive and interoceptive domains modulates self-experience in the rubber-hand illusion

Identifying with a body is central to being a conscious self. The now classic “rubber hand illusion” demonstrates that the experience of body ownership can be modulated by manipulating the timing of exteroceptive(visual and tactile)body-related feedback. Moreover,the strength of this modulation is related to individual differences in sensitivity to internal bodily signals(interoception). However the interaction of exteroceptive and interoceptive signals in determining the experience of body-ownership within an individual remains poorly understood.Here, we demonstrate that this depends on the online integration of exteroceptive and interoceptive signals by implementing an innovative “cardiac rubber hand illusion” that combined computer-generated augmented-reality with feedback of interoceptive (cardiac) information. We show that both subjective and objective measures of virtual-hand ownership are enhanced by cardio-visual feedback in-time with the actual heartbeat,as compared to asynchronous feedback. We further show that these measures correlate with individual differences in interoceptive sensitivity,and are also modulated by the integration of proprioceptive signals instantiated using real-time visual remapping of finger movements to the virtual hand.Our results demonstrate that interoceptive signals directly influence the experience of body ownership via multisensory integration,and they lend support to models of conscious selfhood based on interoceptive predictive coding

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems