Munkmccullough8857

Clinical Relevance- The proposed quality indicator engine helps to increase the efficacy of vital parameter estimation (e.g. heart rate) from pervasive, wrist-worn PPG sensors on the backdrop of motion artifacts when used in ambulatory settings (e.g. activities of daily living).In this work, we demonstrated a Smart Sleep Mask with several integrated physiological sensors such as 3-axis accelerometers, respiratory acoustic sensor, and an eye movement sensor. In particular, using infrared optical sensors, eye movement frequency, direction, and amplitude can be directly monitored and recorded during sleep sessions. We also developed a mobile app for data storage, signal processing and data analytics. Aggregation of these signals from a single wearable device may offer ease of use and more insights for sleep monitoring and REM sleep assessment. The user-friendly mask design can enable at-home use applications in the studies of digital biomarkers for sleep disorder related neurodegenerative diseases. Examples include REM Sleep Behavior Disorder, epilepsy event detection and stroke induced facial and eye movement disorder.Clinical Relevance-Many diseases such as stroke, epilepsy, and Parkinson's disease can cause significant abnormal events during sleep or are associated with sleep disorder. Selleckchem Ac-FLTD-CMK A smart sleep mask may serve as a simple platform to provide various physiological signals and generate clinical meaningful insights by revealing the neurological activities during various sleep stages.Inadvertent lower extremity displacement (ILED) puts the feet of power wheelchair (PWC) users at great risk of traumatic injury. Because disabled individuals may not be aware of a mis-positioned foot, a real-time system for notification can reduce the risk of injury. To test this concept, we developed a prototype system called FootSafe, capable of real-time detection and classification of foot position. The FootSafe system used an array of force-sensing resistors to monitor foot pressures on the PWC footplate. Data were transmitted via Bluetooth to an iOS app which ran a classifier algorithm to notify the user of ILED. In a pilot trial, FootSafe was tested with seven participants seated in a PWC. Data collected from this trial were used to test the accuracy of classification algorithms. A custom figure of merit (FOM) was created to balance the risk of missed positive and false positive. While a machine-learning algorithm (K nearest neighbors, FOM=0.78) outperformed simpler methods, the simplest algorithm, mean footplate pressure, performed similarly (FOM=0.62). In a real-time classification task, these results suggest that foot position can be estimated using relatively few force sensors and simple algorithms running on mobile hardware.Clinical Relevance- Foot collisions or dragging are severe or life-threatening injuries for people with spinal cord injuries. The FootSafe sensor, iOS app, and classifier algorithm can warn the user of a mis-positioned foot to reduce the incidence of injury.Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by dream enactment, abnormal jerks and movements during REM sleep. Isolated RBD (iRBD) is recognized as the early stage of alpha-synucleinopathies, i.e. dementia with Lewy bodies, Parkinson's disease and multiple system atrophy. The certain diagnosis of iRBD requires video-polysomnography, evaluated by experts with time-consuming visual analyses. In this study, we propose automatic analysis of movements detected with 3D contactless video as a promising technology to assist sleep experts in the identification of patients with iRBD. By using automatically detected upper and lower body movements occurring during REM sleep with a duration between 4s and 5s, we could discriminate 20 iRBD patients from 24 patients with sleep-disordered breathing with an accuracy of 0.91 and F1-score of 0.90. This pilot study shows that 3D contactless video can be successfully used as a non-invasive technology to assist clinicians in identifying abnormal movements during REM sleep, and therefore to recognize patients with iRBD. Future investigations in larger cohorts are needed to validate the proposed technology and methodology.The incredible pace at which the world's elderly population is growing will put severe burdens on current healthcare systems and resources. To alleviate this concern the health care systems must rely on the transformation of eldercare and old homes to use Ambient Assisted Living (AAL). Human identification is one of the most common and critical tasks for condition monitoring, human-machine interaction, and providing assistive services in such environments. Recently, human gait has gained new attention as a biometric for identification to achieve contactless identification from a distance robust to physical appearances. However, an important aspect of gait identification through wearables and image-based systems alike is accurate identification when limited information is available for example, when only a fraction of the whole gait cycle or only a part of the subject's body is visible. In this paper, we present a gait identification technique based on temporal and descriptive statistic parameters of different gait phases as the features and we investigate the performance of using only single gait phases for the identification task using a minimum number of sensors. Gait data were collected from 60 individuals through pelvis and foot sensors. Six different machine learning algorithms were used for identification. It was shown that it is possible to achieve high accuracy of over 95.5% by monitoring a single phase of the whole gait cycle through only a single sensor. It was also shown that the proposed methodology could be used to achieve 100% identification accuracy when the whole gait cycle was monitored through pelvis and foot sensors combined. The ANN was found to be more robust to less number of data features compared to SVM and was concluded as the best machine algorithm for the purpose.Psychological stress (PS) in daily life can trigger acute changes in cardiovascular function and may lead to increased risk of cardiovascular problems. Prior laboratory-based studies provide little evidence on temporal changes in the associations between PS and cardiovascular responses in natural settings. We hypothesized that daily PS would be associated with higher heart rate (HR) and lower heart rate variability (HRV). Using smartphones, ten participants (four females, 21.1±1.1 years) completed ecological momentary assessment (EMA) 6 times a day for two weeks regarding their current affective state. Participants rated levels of PS, as well as 3 high-arousal negative affect (HNA Anxious, Annoyed, and Upset), and 3 low-arousal negative affect (LNA Sluggish, Bored, and Sad) states. They also wore a chest-mounted heart-rate monitor and a wrist accelerometer to monitor cardiovascular response and physical activity, respectively. HR and HRV variables in the time intervals (5, 30, 60 min) before and after EMA were used as indicators of cardiovascular response. Multilevel modeling was used to examine the association between affect and HR/HRV, controlling for physical activity. Higher HR and lower HRV were related to subsequent greater feelings of stress at the 5 and 30-min time intervals. No significant associations were observed between cardiovascular parameters and subsequent affective states, suggesting that the acute exaggerated cardiovascular responses occurred due to PS. Higher LNA was related to antecedent/subsequent lower HR or higher HRV within 2 hours, while HNA was unrelated to HR or HRV for all time intervals, suggesting that both high/low arousal NA were not related to exaggerated cardiovascular response. Understanding psychological feelings of stress and LNA may be helpful in the management of daily cardiovascular health.Healthy cholinergic function is important for brain function, and disruption of the system is thought to be the cause of dementia, including Alzheimer's disease. The 'Cholinergic Hypothesis' theorizes that cognitive decline is due disruption of the cholinergic system, defined by the low concentration of neurotransmitters such as acetylcholine (ACh) and neurotransmitter-releasing elements such as calcium ions (Ca2+). The ability to measure ACh and Ca2+ concentrations enables researchers to make inferences on the relationship between these indicators that play a role in the onset of neurological conditions. Current commercial devices have one or more of the following limitations i) they are tethered making it difficult to verify in naturally behaving animal subjects, ii) they are capable of only measuring a single indicator at any given time, or iii) they have multiple shanks that penetrate the cortex. We propose a tri-color miniaturized photometry system capable of optically stimulating indicators in neurons located in the hippocampus and basal forebrain and optically reading the neurons' response. The resulting device has an average gain of 123 dB and a power consumption of 29 mW, comparable to other state-of-the-art devices.Meal timing affects metabolic responses to diet, but participant compliance in time-restricted feeding and other diet studies is challenging to monitor and is a major concern for research rigor and reproducibility. To facilitate automated validation of participant self-reports of meal timing, the present study focuses on the creation of a meal detection algorithm using continuous glucose monitoring (CGM), physiological monitors and machine learning. While most CGM-related studies focus on participants who are diabetic, this study is the first to apply machine learning to meal detection using CGM in metabolically healthy adults. Furthermore, the results demonstrate a high area under the receiver operating characteristic curve (AUC-ROC) and precision-recall curve (AUC-PR). A cold-start simulation using a random forest algorithm yields .891 and .803 for AUC-ROC and AUC-PR respectively on 110-minutes data, and a non-cold start simulation using a gradient boosted tree model yields over .996 (AUC-ROC) and .964 (AUC-PR). Here it is demonstrated that CGM and physiological monitoring data is a viable tool for practitioners and scientists to objectively validate self-reports of meal consumption in healthy participants.Human Activity Recognition (HAR), using machine learning to identify times spent (for example) walking, sitting, and standing, is widely used in health and wellness wearable devices, in ambient assistant living devices, and in rehabilitation. In this paper, a stacked Long Short-Term Memory (LSTM) structure is designed for HAR to be implemented on a smartphone. The use of an edge device for the processing means that the raw collected data does not need to be passed to the cloud for processing, mitigating potential bandwidth, power consumption, and privacy concerns. Our offline prototype model achieves 92.8% classification accuracy when classifying 6 activities using a public dataset. Quantization techniques are shown to reduce the model's weight representations to achieve a >30x model size reduction for improved use on a smartphone. The end result is an on-phone HAR model with accuracy of 92.7% and a memory footprint of 27 KB.