Artificial intelligence and machine learning techniques have progressed dramatically and become powerful tools required to solve complicated tasks, such as computer vision, speech recognition, and natural language processing. Since these techniques have provided promising and evident results in these fields, they emerged as valuable methods for applications in human physiology and healthcare. General physiological recordings are time-related expressions of bodily processes associated with health or morbidity. Sequence classification, anomaly detection, decision making, and future status prediction drive the learning algorithms to focus on the temporal pattern and model the nonstationary dynamics of the human body. These practical requirements give birth to the use of recurrent neural networks (RNNs), which offer a tractable solution in dealing with physiological time series and provide a way to understand complex time variations and dependencies. The primary objective of this article is to provide an overview of current applications of RNNs in the area of human physiology for automated prediction and diagnosis within different fields. Finally, we highlight some pathways of future RNN developments for human physiology.

Abstract Gait abnormalities are typically derived from neurological conditions or orthopaedic problems and can cause severe consequences such as limited mobility and falls. Gait analysis plays a crucial role in monitoring gait abnormalities and discovering underlying deficits can help develop rehabilitation programs. Contemporary gait analysis requires a multi-modal gait analysis approach where spatio-temporal, kinematic and muscle activation gait characteristics are investigated. Additionally, protocols for gait analysis are going beyond labs/clinics to provide more habitual insights, uncovering underlying reasons for limited mobility and falls during daily activities. Wearables are the most prominent technology that are reliable and allow multi-modal gait analysis beyond the labs/clinics for extended periods. There are established wearable-based algorithms for extracting informative gait characteristics and interpretation. This paper proposes a multi-layer fusion framework with sensor, data and gait characteristics. The wearable sensors consist of four units (inertial and electromyography, EMG) attached to both legs (shanks and thighs) and surface electrodes placed on four muscle groups. Inertial and EMG data are interpreted by numerous validated algorithms to extract gait characteristics in different environments. This paper also includes a pilot study to test the proposed fusion approach in a small cohort of stroke survivors. Experimental results in various terrains show healthy participants experienced the highest pace and variability along with slightly increased knee flexion angles (≈1°) and decreased overall muscle activation level during outdoor walking compared to indoor, incline walking activities. Stroke survivors experienced slightly increased pace, asymmetry, and knee flexion angles (≈4°) during outdoor walking compared to indoor. A multi-modal approach through a sensor, data and gait characteristic fusion presents a more holistic gait assessment process to identify changes in different testing environments. The utilisation of the fusion approach presented here warrants further investigation in those with neurological conditions, which could significantly contribute to the current understanding of impaired gait.

BACKGROUND Identifying older adults with risk for falls prior to discharge home from the Emergency Department (ED) could help direct fall prevention interventions, yet ED-based tools to assist risk stratification are under-developed. The aim of this study was to assess the performance of self-report and functional assessments to predict falls in the 3 months post-ED discharge for older adults. METHODS A prospective cohort of community-dwelling adults age 60 years and older were recruited from one urban ED (N = 134). Participants completed: a single item screen for mobility (SIS-M), the 12-item Stay Independent Questionnaire (SIQ-12), and the Timed Up and Go test (TUG). Falls were defined through self-report of any fall at 1- and 3-months and medical record review for fall-related injury 3-months post-discharge. We developed a hybrid-convolutional recurrent neural network (HCRNN) model of gait and balance characteristics using truncal 3-axis accelerometry collected during the TUG. Internal validation was conducted using bootstrap resampling with 1000 iterations for SIS-M, FRQ, and GUG and leave-one-out for the HCRNN. We compared performance of M-SIS, FRQ, TUG time, and HCRNN by calculating the area under the receiver operating characteristic area under the curves (AUCs). RESULTS 14 (10.4%) of participants met our primary outcome of a fall or fall-related injury within 3-months. The SIS-M had an AUC of 0.42 [95% confidence interval (CI) 0.19-0.65]. The SIQ-12 score had an AUC of 0.64 [95% confidence interval (CI) 0.49-0.80]. The TUG had an AUC of 0.48 (95% CI 0.29-0.68). The HCRNN model using generated accelerometer features collected during the TUG had an AUC of 0.99 (95% CI 0.98-1.00). CONCLUSION We found that self-report and functional assessments lack sufficient accuracy to be used in isolation in the ED. A neural network model using accelerometer features could be a promising modality but research is needed to externally validate these findings.

Dysphagia, commonly referred to as abnormal swallowing, affects millions of people annually. If not diagnosed expeditiously, dysphagia can lead to more severe complications, such as pneumonia, nutritional deficiency, and dehydration. Bedside screening is the first step of dysphagia characterization and is usually based on pass/fail tests in which a nurse observes the patient performing water swallows to look for dysphagia overt signs such as coughing. Though quick and convenient, bedside screening only provides low-level judgment of impairment, lacks standardization, and suffers from subjectivity. Recently, high resolution cervical auscultation (HRCA) has been investigated as a less expensive and non-invasive method to diagnose dysphagia. It has shown strong preliminary evidence of its effectiveness in penetration-aspiration detection as well as multiple swallow kinematics. HRCA signals have traditionally been collected and investigated in conjunction with videofluoroscopy exams which are performed using barium boluses including thin liquid. An HRCA-based bedside screening is highly desirable to expedite the initial dysphagia diagnosis and overcome all the drawbacks of the current pass/fail screening tests. However, all research conducted for using HRCA in dysphagia is based on thin liquid barium boluses and thus not guaranteed to provide valid results for water boluses used in bedside screening. If HRCA signals show no significant differences between water and thin liquid barium boluses, then the same algorithms developed on thin liquid barium boluses used in diagnostic imaging studies, it can be then directly used with water boluses. This study investigates the similarities and differences between HRCA signals from thin liquid barium swallows compared to those signals from water swallows. Multiple features from the time, frequency, time-frequency, and information-theoretic domain were extracted from each type of swallow and a group of linear mixed models was tested to determine the significance of differences. Machine learning classifiers were fit to the data as well to determine if the swallowed material (thin liquid barium or water) can be correctly predicted from an unlabeled set of HRCA signals. The results demonstrated that there is no systematic difference between the HRCA signals of thin liquid barium swallows and water swallows. While no systematic difference was discovered, the evidence of complete conformity between HRCA signals of both materials was inconclusive. These results must be validated further to confirm conformity between the HRCA signals of thin liquid barium swallows and water swallows.

Abstract Community mobility involves walking with physical and cognitive challenges. In older adults (N=116; results here from initial analyses: N=29, Age=75±5 years, 51% females), we assessed gait speed and smoothness (harmonic-ratio) while walking on even and uneven surfaces, with or without an alternate alphabeting dual-task (ABC). ANOVA assessed surface and dual-task effects; Pearson correlations compared gait with global cognition and executive function composite z-scores. The four conditions (even, uneven, even-ABC and uneven-ABC) affected speed(m/s) (0.97±0.14 vs 0.90±0.15 vs 0.83±0.17 vs 0.79±0.16). Smoothness (2.19±0.48 vs 1.89±0.38 vs 1.92±0.53 vs 1.7±0.43) was affected by only surface (controlled for speed). Greater speed was associated with better global cognition(ρ=0.47 to 0.49, p<0.05) for all conditions and with better executive function for even-ABC(ρ=0.39, p=0.04) and uneven-ABC(ρ=0.40, p=0.03). Executive function was associated with smoothness during even(ρp=-0.42, p=0.03) and uneven(ρp=-0.39, p=0.04) walking. Type of walking challenge differentially affects gait quality and associations with cognitive function.

Dual-task balance studies explore interference between balance and cognitive tasks. This study is a descriptive analysis of accelerometry balance metrics to determine if a verbal cognitive task influences postural control after the task ends. Fifty-two healthy older adults (75 ± 6 years old, 30 female) performed standing balance and cognitive dual-tasks. An accelerometer recorded movement from before, during, and after the task (reciting every other letter of the alphabet). Thirty-six balance metrics were calculated for each task condition. The effect of the cognitive task on postural control was determined by a generalized linear model. Twelve variables, including anterior–posterior centroid frequency, peak frequency and entropy rate, medial-later entropy rate and wavelet entropy, and bandwidth in all directions, exhibited significant differences between baseline and cognitive task periods, but not between baseline and post-task periods. These results indicate that the verbal cognitive task did alter balance, but did not bring about persistent effects after the task had ended. Traditional balance measurements, i.e., root mean square and normalized path length, notably lacked significance, highlighting the potential to use other accelerometer metrics for the early detection of balance problems. These novel insights into the temporal dynamics of dual-task balance support current dual-task paradigms to reduce fall risk in older adults.

There is growing enthusiasm to develop inexpensive, non-invasive, and portable methods that accurately assess swallowing and provide biofeedback during dysphagia treatment. High-resolution cervical auscultation (HRCA), which uses acoustic and vibratory signals from non-invasive sensors attached to the anterior laryngeal framework during swallowing, is a novel method for quantifying swallowing physiology via advanced signal processing and machine learning techniques. HRCA has demonstrated potential as a dysphagia screening method and diagnostic adjunct to VFSSs by determining swallowing safety, annotating swallow kinematic events, and classifying swallows between healthy participants and patients with a high degree of accuracy. However, its feasibility as a non-invasive biofeedback system has not been explored. This study investigated 1. Whether HRCA can accurately differentiate between non-effortful and effortful swallows; 2. Whether differences exist in Modified Barium Swallow Impairment Profile (MBSImP) scores (#9, #11, #14) between non-effortful and effortful swallows. We hypothesized that HRCA would accurately classify non-effortful and effortful swallows and that differences in MBSImP scores would exist between the types of swallows. We analyzed 247 thin liquid 3 mL command swallows (71 effortful) to minimize variation from 36 healthy adults who underwent standardized VFSSs with concurrent HRCA. Results revealed differences (p < 0.05) in 9 HRCA signal features between non-effortful and effortful swallows. Using HRCA signal features as input, decision trees classified swallows with 76% accuracy, 76% sensitivity, and 77% specificity. There were no differences in MBSImP component scores between non-effortful and effortful swallows. While preliminary in nature, this study demonstrates the feasibility/promise of HRCA as a biofeedback method for dysphagia treatment.

Aspiration is a serious complication of swallowing disorders. Adequate detection of aspiration is essential in dysphagia management and treatment. High-resolution cervical auscultation has been increasingly considered as a promising noninvasive swallowing screening tool and has inspired automatic diagnosis with advanced algorithms. The performance of such algorithms relies heavily on the amount of training data. However, the practical collection of cervical auscultation signal is an expensive and time-consuming process because of the clinical settings and trained experts needed for acquisition and interpretations. Furthermore, the relatively infrequent incidence of severe airway invasion during swallowing studies constrains the performance of machine learning models. Here, we produced supplementary training exemplars for desired class by capturing the underlying distribution of original cervical auscultation signal features using auxiliary classifier Wasserstein generative adversarial networks. A 10-fold subject cross-validation was conducted on 2079 sets of 36-dimensional signal features collected from 189 patients undergoing swallowing examinations. The proposed data augmentation outperforms basic data sampling, cost-sensitive learning and other generative models with significant enhancement. This demonstrates the remarkable potential of proposed network in improving classification performance using cervical auscultation signals and paves the way of developing accurate noninvasive swallowing evaluation in dysphagia care.

BACKGROUND Novel temporal-spatial features of the 12‑lead ECG can conceptually optimize culprit lesions' detection beyond that of classical ST amplitude measurements. We sought to develop a data-driven approach for ECG feature selection to build a clinically relevant algorithm for real-time detection of culprit lesion. METHODS This was a prospective observational cohort study of chest pain patients transported by emergency medical services to three tertiary care hospitals in the US. We obtained raw 10-s, 12‑lead ECGs (500 s/s, HeartStart MRx, Philips Healthcare) during prehospital transport and followed patients 30 days after the encounter to adjudicate clinical outcomes. A total of 557 global and lead-specific features of P-QRS-T waveform were harvested from the representative average beats. We used Recursive Feature Elimination and LASSO to identify 35/557, 29/557, and 51/557 most recurrent and important features for LAD, LCX, and RCA culprits, respectively. Using the union of these features, we built a random forest classifier with 10-fold cross-validation to predict the presence or absence of culprit lesions. We compared this model to the performance of a rule-based commercial proprietary software (Philips DXL ECG Algorithm). RESULTS Our sample included 2400 patients (age 59 ± 16, 47% female, 41% Black, 10.7% culprit lesions). The area under the ROC curves of our random forest classifier was 0.85 ± 0.03 with sensitivity, specificity, and negative predictive value of 71.1%, 84.7%, and 96.1%. This outperformed the accuracy of the automated interpretation software of 37.2%, 95.6%, and 92.7%, respectively, and corresponded to a net reclassification improvement index of 23.6%. Metrics of ST80; Tpeak-Tend; spatial angle between QRS and T vectors; PCA ratio of STT waveform; T axis; and QRS waveform characteristics played a significant role in this incremental gain in performance. CONCLUSIONS Novel computational features of the 12‑lead ECG can be used to build clinically relevant machine learning-based classifiers to detect culprit lesions, which has important clinical implications.

Swallowing physiology includes numerous biomechanical events including displacement of the hyoid bone, which is a crucial component of airway protection and opening of the proximal esophagus. The objective of this study was to evaluate the potential relations between the trajectory of hyoid bone movement and the risk of airway penetration and aspiration during a videofluoroscopic swallowing study. Two hundred sixty-five patients were involved in this study, producing a total of 1433 swallows of various volumes consisting of thin liquid, nectar-thick liquid, and solids during a fluoroscopic exam. The anterior and posterior landmarks of the body of the hyoid bone were manually marked in each frame of each fluoroscopic video. Generalized estimation equations were applied to evaluate the relationship between penetration–aspiration scores and mathematical features extracted from the hyoid bone trajectories, while also considering the influence of other independent variables such as age, bolus volume, and viscosity. Our results indicated that penetration–aspiration scores showed a significant relation to age. The maximum anterior (horizontal) displacement of the anterior hyoid bone landmark was significantly associated with the penetration–aspiration scores. Differences in the displacement of the hyoid bone are useful observations in airway protection. In this work, the potential relations between the trajectory of hyoid bone movement and the risk of airway penetration and aspiration during a videofluoroscopic swallowing study were evaluated. We extracted features from the hyoid bone trajectories and applied generalized estimation equations to investigate their relationship to penetration–aspiration scales. The results showed that the maximum anterior (horizontal) displacement of the anterior hyoid bone landmark was significantly associated with the penetration–aspiration scales. In this work, the potential relations between the trajectory of hyoid bone movement and the risk of airway penetration and aspiration during a videofluoroscopic swallowing study were evaluated. We extracted features from the hyoid bone trajectories and applied generalized estimation equations to investigate their relationship to penetration–aspiration scales. The results showed that the maximum anterior (horizontal) displacement of the anterior hyoid bone landmark was significantly associated with the penetration–aspiration scales.

In this manuscript, distance and position estimation problems are investigated for visible light positioning (VLP) systems with red-green-blue (RGB) light emitting diodes (LEDs). The accuracy limits on distance and position estimation are calculated in terms of the Cramer-Rao lower bound (CRLB) for three different scenarios. Scenario~1 and Scenario~2 correspond to synchronous and asynchronous systems, respectively, with known channel attenuation formulas at the receiver. In Scenario~3, a synchronous system is considered but channel attenuation formulas are not known at the receiver. The derived CRLB expressions reveal the relations among distance/position estimation accuracies in the considered scenarios and lead to intuitive explanations for the benefits of using RGB LEDs. In addition, maximum likelihood (ML) estimators are derived in all scenarios, and it is shown that they can achieve close performance to the CRLBs in some cases for sufficiently high source optical powers.

BACKGROUND falls and fall-related injuries are common in older adults, have negative effects both on quality of life and functional independence and are associated with increased morbidity, mortality and health care costs. Current clinical approaches and advice from falls guidelines vary substantially between countries and settings, warranting a standardised approach. At the first World Congress on Falls and Postural Instability in Kuala Lumpur, Malaysia, in December 2019, a worldwide task force of experts in falls in older adults, committed to achieving a global consensus on updating clinical practice guidelines for falls prevention and management by incorporating current and emerging evidence in falls research. Moreover, the importance of taking a person-centred approach and including perspectives from patients, caregivers and other stakeholders was recognised as important components of this endeavour. Finally, the need to specifically include recent developments in e-health was acknowledged, as well as the importance of addressing differences between settings and including developing countries. METHODS a steering committee was assembled and 10 working Groups were created to provide preliminary evidence-based recommendations. A cross-cutting theme on patient's perspective was also created. In addition, a worldwide multidisciplinary group of experts and stakeholders, to review the proposed recommendations and to participate in a Delphi process to achieve consensus for the final recommendations, was brought together. CONCLUSION in this New Horizons article, the global challenges in falls prevention are depicted, the goals of the worldwide task force are summarised and the conceptual framework for development of a global falls prevention and management guideline is presented.