Apple Watch Sleep and Physiological Tracking Compared to Clinically Validated Actigraphy, Ballistocardiography and Polysomnography.

This paper investigates the performance of the latest Apple Watch (Series 8, released September 2022) in comparison with research grade devices. The Apple Watch was compared to wrist worn actigraphy, non-contact ballistocardiography (BCG) placed in the bed and evaluated with polysomnography (PSG) as a reference system. Sleep analysis and individual cardiorespiratory parameters were measured from the Apple Watch. The Apple Watch performed well for identifying sleep-wake states but had difficulty identifying the sleep stages compared to the reference PSG system. Physiological parameters obtained from the Apple Watch compared well with measurements of the other devices in the study.Clinical Relevance- Consumer devices are readily available and inexpensive compared to clinical devices. A consumer device that can provide accurate physiological data equivalent to a clinical device would let researchers and clinicians collect data without the intrusive nature of a clinical device.

Decoding Brain Signals to Classify Gait Direction Anticipation.

The use of brain-computer interface (BCI) technology has emerged as a promising rehabilitation approach for patients with motor function and motor-related disorders. BCIs provide an augmentative communication platform for controlling advanced assistive robots such as a lower-limb exoskeleton. Brain recordings collected by an electroencephalography (EEG) system have been employed in the BCI platform to command the exoskeleton. To date, the literature on this topic is limited to the prediction of gait intention and gait variations from EEG signals. This study, however, aims to predict the anticipated gait direction using a stream of EEG signals collected from the brain cortex. Three healthy participants (age range: 29-31, 2 female) were recruited. While wearing the EEG device, the participants were instructed to initiate gait movement toward the direction of the arrow triggers (pointing forward, backward, left, or right) being shown on a screen with a blank white background. Collected EEG data was then epoched around the trigger timepoints. These epochs were then converted to the time-frequency domain using event- related synchronization (ERS) and event-related desynchronization (ERD) methods. Finally, the classification pipeline was constructed using logistic regression (LR), support vector machine (SVM), and convolutional neural network (CNN). A ten-fold cross-validation scheme was used to evaluate the classification performance. The results revealed that the CNN classifier outperforms the other two classifiers with an accuracy of 0.75. Clinical Relevance - The outcome of this study has the potential to be ultimately used for interactive navigation of the lower-limb exoskeletons during robotic rehabilitation therapy and enhance neurodegeneration and neuroplasticity in a wide range of individuals with lower-limb motor function disabilities.

Paraganglioma masquerading as a primary liver lesion: A rare entity discovered during surgery.

A 54-year-old woman with controlled hypertension presented with abdominal pain and weight loss. Imaging revealed a 6.6 cm liver lesion. During resection, she became severely hypertensive and diagnosis was paraganglioma. Hepatic paragangliomas are exceedingly rare but must be considered in the differential of abdominal mass even without typical clinical symptoms.

Impairment Screening Utilizing Biophysical Measurements and Machine Learning Algorithms.

Drug recognition expert (DRE) officers employ a set of tests to investigate drivers who are under impairment and to determine the type of drug that they have used. Horizontal Gaze Nystagmus (HGN), Walk and Turn (WAT), and One Leg Stand (OLS) are the main three tests included in the Standardized Field Sobriety Tests (SFSTs), which lead the officers to evaluate the sobriety of drivers. Performing these tests requires trained officers, but the final decision may still be subjective. These tests along with a suite of comprehensive (yet manual) at-station testing are the basis of police decision making and are subjected to scrutiny by courts. Therefore, designing an automated system to detect impairment not only will help officers in making accurate decisions, but also will remove the subjectivity and can potentially serve as a court-admissible evidence. In this paper, a new method for automated impairment detection is introduced and implemented using data analysis and machine learning algorithms based on a comprehensive suite of tests performed on 34 participants.

Improving Energy Expenditure Estimation through Activity Classification and Walking Speed Estimation Using a Smartwatch.

Energy expenditure (EE) estimation is an important factor in tracking personal activity and preventing chronic diseases, such as obesity and diabetes. The challenge is to provide accurate EE estimations in free-living environment through portable and unobtrusive devices. In this paper, we present an experimental study to estimate energy expenditure during sitting, standing and treadmill walking using a smartwatch. We introduce a novel methodology, which aims to improve the EE estimation by first separating sedentary (sitting and standing) and non-sedentary (walking) activities, followed by estimating the walking speeds and then calculating the energy expenditure using advanced machine learning based regression models. Ten young adults participated in the experimental trials. Our results showed that combining activity type and walking speed information with the acceleration counts substantially improved the accuracy of regression models for estimating EE. On average, the activity-based models provided 7% better EE estimation than the traditional acceleration-based models.

Differentiating Sitting, Standing, and Walking Through Regional Plantar Pressure Characteristics.

Prolonged static weight bearing (WBR) is thought to aggravate plantar heel pain and is common in the workplace, which may put employees at greater risk of developing plantar heel pain. However, objective measures of physical activity and sedentary behaviors in the workplace are lacking, making it difficult to establish or refute the connection between work exposure and plantar heel pain. Characterizing loading patterns during common workplace postures will enhance the understanding of foot function and inform the development of new measurement tools. Plantar pressure data during periods of sitting, standing, and walking were measured in ten healthy participants using the F-Scan in-shoe measurement system (Tekscan Inc, Boston, MA). Peak and average pressure, peak and average contact area, and average pressure differential were analyzed in ten different regions of the foot. A two-way repeated measures analysis of variance (ANOVA) assessed the posture by foot region interaction for each measurement parameter; significant effects of posture by foot region were identified for all five measurement parameters. Ten foot region by measurement parameter combinations were found to significantly differentiate all three postures simultaneously; seven used pressure measures to differentiate while three used area measures. The heel, lateral midfoot (LM), and medial and central forefoot (CFF) encompassed nine of ten areas capable of differentiating all postures simultaneously. This work demonstrates that plantar pressure is a viable means to characterize and differentiate three common workplace postures. The results of this study can inform the development of measurement tools for quantifying posture duration at work.

Development of a microfluidic platform for size-based hydrodynamic enrichment and PSMA-targeted immunomagnetic isolation of circulating tumour cells in prostate cancer.

Efforts to further improve the clinical management of prostate cancer (PCa) are hindered by delays in diagnosis of tumours and treatment deficiencies, as well as inaccurate prognoses that lead to unnecessary or inefficient treatments. The quantitative and qualitative analysis of circulating tumour cells (CTCs) may address these issues and could facilitate the selection of effective treatment courses and the discovery of new therapeutic targets. Therefore, there is much interest in isolation of elusive CTCs from blood. We introduce a microfluidic platform composed of a multiorifice flow fractionation (MOFF) filter cascaded to an integrated microfluidic magnetic (IMM) chip. The MOFF filter is primarily employed to enrich immunomagnetically labeled blood samples by size-based hydrodynamic removal of free magnetic beads that must originally be added to samples at disproportionately high concentrations to ensure the efficient immunomagnetic labeling of target cancer cells. The IMM chip is then utilized to capture prostate-specific membrane antigen-immunomagnetically labeled cancer cells from enriched samples. Our preclinical studies showed that the proposed method can selectively capture up to 75% of blood-borne PCa cells at clinically-relevant low concentrations (as low as 5 cells/ml), with the IMM chip showing up to 100% magnetic capture capability.

Detection of Sleep and Wake States Based on the Combined Use of Actigraphy and Ballistocardiography.

This paper presents a combined use of actigraphy and ballistocardiography to measure sleep stages without a disruptive sleep environment. Although polysomnography (PSG) is considered the gold standard for measuring sleep stages, the intrusive setup may lead to an unnatural sleep and impact the actual sleep quality. To address this issue a novel approach to measure sleep stages is presented by combining the acceleration measurements of actigraphy with the cardiological measurements of ballistocardiography. The combined measurements are compared with PSG for verification of the closest match possible with minimal interference for the sleeping individual. The experimental results of the study show that sleep/wake states can be classified accurately using the integrated approach.

Combined Regression and Classification Models for Accurate Estimation of Walking Speed Using a Wrist-worn IMU.

Walking speed is an important quantity not only in fitness applications but also for Iifestyle and health monitoring purposes. With the recent advances in MEMS technology, miniature body-worn sensors have been used for ambulatory walking speed estimation using regression models. However, studies show that these models are more prone to errors in slow walking regime compared to normal and fast walking regimes. To address this issue, our study proposes a combined classification and regression walking speed estimation model. An experimental evaluation was performed on 10 healthy subjects during treadmill walking trials using a smartwatch. The experimental results show that including the classification model can improve the accuracy of walking speed estimation in the slow speed regime by about 22%. The results show that the proposed combined model has error of less than around 13% for various walking speed regimes.

Use of Average Vertical Velocity and Difference in Altitude for Improving Automatic Fall Detection from Trunk Based Inertial and Barometric Pressure Measurements.

Despite the extensive research that has been carried out on automatic fall detection using wearable sensors, falls in the elderly cannot be detected effectively yet. Although recent fall detection algorithms that evaluate the descent, impact and post impact phases of falls, often using vertical velocity, vertical acceleration and trunk angle respectively, tend to be more accurate than the algorithms that do not consider them, they still lack the desired accuracy required to be used among frail older adults. This study aims to improve the accuracy of fall detection algorithms by incorporating average vertical velocity and difference in altitude as additional parameters to the vertical velocity, vertical acceleration and trunk angle parameters. We tested the proposed algorithms on data recorded from a comprehensive set of falling experiments with 12 young participants. Participants wore waist-mounted accelerometer, gyroscope and barometric pressure sensors and simulated the most common types of falls observed in older adults, along with near-falls and activities of daily living (ADLs). Our results showed that, while the base algorithm with the three parameters provided 91.8% specificity, the addition of difference in altitude and average vertical velocity improved the specificity to 98.0% and 99.6%, respectively.

Epithelioid angiomyolipoma metastasis to the rectus abdominis.

A 37-year-old female presented with abdominal pain. An abdominal computed tomography scan demonstrated a 10 cm x 13 cm left renal mass. An open adrenal-sparing radical nephrectomy was performed. The pathological diagnosis was epithelioid angiomyolipoma. Five-year surveillance did not demonstrate recurrence of disease. However, a 1.8 cm x 2.5 cm mass on the rectus abdominis muscle was identified after 5 years. Biopsy of the mass demonstrated histologic findings consistent with the primary tumor. Herein, we report a case of metastatic renal epithelioid angiomyolipoma to the rectus abdominis muscle more than 5 years after resection of primary renal tumor.

Vital Signs and Physiologic Derangement in Patients With Thoracic Trauma in Iraq and Afghanistan.

BACKGROUND: Triage is the act of stratifying the need for medical attention. Effective triage must account for injury patterns and severity. Personnel making triage decisions must also consider the patients' physiologic states. Vital signs can possibly be used to assess for the presence of physiological derangements such as coagulopathy, acidosis, or a significant base deficit. Providers could use this knowledge to assist with triage at casualty collection points where laboratory studies or point of care testing may not be available. METHODS: With institutional approval, data were extracted from the Joint Theater Trauma Registry for all patients with thoracic trauma between 2002 and 2012. Patients were identified by International Statistical Classification of Diseases and Related Health Problems, 9th Revision (ICD-9) codes. Heart rate (HR), systolic blood pressure (SBP), and pulse pressure were correlated with coagulopathy (international normalization ratio ≥ 1.5), acidosis (pH < 7.2) or an elevated base deficit (>6) on admission. Sensitivity, specificity, positive predictive values, negative predictive values, and odds ratios were calculated. FINDINGS: HR > 100, SBP < 90, or pulse pressure <30 were associated with an increased risk for acidosis (odds ratio 3.06 [95% confidence interval 2.48-3.78], 4.72 [3.85-5.78], and 2.73 [2.15-3.48], respectively), coagulopathy (2.21 [1.72-2.83], 4.55 [3.57-5.80], and 2.73 [2.15-3.48], respectively), and base deficit >6 (2.17 [1.88-2.50], 3.48 [2.87-4.22], and 2.22 [1.78-2.77], respectively). HR was a moderately sensitive marker (0.74), whereas SBP was a specific marker (0.93). DISCUSSION: SBP < 90 is an effective marker for ruling in physiologic derangement after thoracic trauma. HR > 100 was associated with over twice the odds for physiologic derangement. Vital signs can be used to assess for physiologic derangement in the population studied and may help in triage.

Accuracy of a wavelet-based fall detection approach using an accelerometer and a barometric pressure sensor.

GOAL: Falls are a major source of morbidity in older adults, and 50% of older adults who fall cannot rise independently after falling. Wearable sensor-based fall detection devices may assist in preventing long lies after falls. The goal of this study was to determine the accuracy of a novel wavelet-based approach to automatically detect falls based on accelerometer and barometric pressure sensor data. METHODS: Participants (n=15) mimicked a range of falls, near falls, and activities of daily living (ADLs) while wearing accelerometer and barometric pressure sensors on the lower back, chest, wrists and thighs. The wavelet transform using pattern adapted wavelets was applied to detect falls from the sensor data. RESULTS: In total, 525 trials (194 falls, 105 near-falls and 226 ADLs) were included in our analysis. When we applied the wavelet-based method on only accelerometer data, classification accuracies ranged from 82% to 96%, with the chest sensor providing the highest accuracy. Accuracy improved by 3.4% on average (p=0.041; SD=3.0%) when we also included the barometric pressure sensor data. The highest classification accuracies (of 98%) were achieved when we combined wavelet-based features and traditional statistical features in a multiphase fall detection model using machine learning. CONCLUSION: We show that the wavelet-based approach accurately distinguishes falls from near-falls and ADLs, and that it can be applied on wearable sensor data generated from various body locations. Additionally, we show that the accuracy of a wavelet-based fall detection system can be further improved by combining accelerometer and barometric pressure sensor data, and by incorporating wavelet and statistical features in a machine learning classification algorithm.

A Gaussian process regression model for walking speed estimation using a head-worn IMU.

Miniature inertial sensors mainly worn on waist, ankle and wrist have been widely used to measure walking speed of the individuals for lifestyle and/or health monitoring. Recent emergence of head-worn inertial sensors in the form of a smart eyewear (e.g. Recon Jet) or a smart ear-worn device (e.g. Sensixa e-AR) provides an opportunity to use these sensors for estimation of walking speed in real-world environment. This work studies the feasibility of using a head-worn inertial sensor for estimation of walking speed. A combination of time-domain and frequency-domain features of tri-axial acceleration norm signal were used in a Gaussian process regression model to estimate walking speed. An experimental evaluation was performed on 15 healthy subjects during free walking trials in an indoor environment. The results show that the proposed method can provide accuracies of better than around 10% for various walking speed regimes. Additionally, further evaluation of the model for long (15-minutes) outdoor walking trials reveals high correlation of the estimated walking speed values to the ones obtained from fusion of GPS with inertial sensors.

Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets.

Falls are a major cause of injuries and deaths in older adults. Even when no injury occurs, about half of all older adults who fall are unable to get up without assistance. The extended period of lying on the floor often leads to medical complications, including muscle damage, dehydration, anxiety and fear of falling. Wearable sensor systems incorporating accelerometers and/or gyroscopes are designed to prevent long lies by automatically detecting and alerting care providers to the occurrence of a fall. Research groups have reported up to 100% accuracy in detecting falls in experimental settings. However, there is a lack of studies examining accuracy in the real-world setting. In this study, we examined the accuracy of a fall detection system based on real-world fall and non-fall data sets. Five young adults and 19 older adults went about their daily activities while wearing tri-axial accelerometers. Older adults experienced 10 unanticipated falls during the data collection. Approximately 400 hours of activities of daily living were recorded. We employed a machine learning algorithm, Support Vector Machine (SVM) classifier, to identify falls and non-fall events. We found that our system was able to detect 8 out of the 10 falls in older adults using signals from a single accelerometer (waist or sternum). Furthermore, our system did not report any false alarm during approximately 28.5 hours of recorded data from young adults. However, with older adults, the false positive rate among individuals ranged from 0 to 0.3 false alarms per hour. While our system showed higher fall detection and substantially lower false positive rate than the existing fall detection systems, there is a need for continuous efforts to collect real-world data within the target population to perform fall validation studies for fall detection systems on bigger real-world fall and non-fall datasets.

Metastatic Adenocarcinoma of the Prostate Presenting as Supraclavicular and Bulky Generalized Lymphadenopathy with a Benign Digital Rectal Exam.

A 63-year-old male presented with complaints of an enlarging left supraclavicular mass and weight loss. Computed tomography demonstrated a large retroperitoneal mass causing displacement of the adjacent organs, and moderate left hydroureteronephrosis. Multiple pulmonary nodules, lytic spinal lesions, and generalized lymphadenopathy including the left supraclavicular region were seen. Serum prostate-specific antigen level was 2064.0 ng/mL. Digital rectal exam revealed an enlarged prostate without nodularity. Biopsy of the supraclavicular node demonstrated prostatic adenocarcinoma. The diagnosis of lymphoma may be initially suggested, however, prostatic origin should be considered even when the prostate exam is not grossly abnormal.

Design and performance analysis of a 3-RRR spherical parallel manipulator for hip exoskeleton applications.

This paper presents the design and performance analysis and experimental study of a 3-RRR spherical parallel manipulator in the context of hip exoskeleton applications. First, the mechanism's inverse kinematics analysis and Jacobian matrix development are revisited. Manipulability, dexterity, and rotational sensitivity indices are then evaluated for two different methods of attachment to the human body. The superior attachment method in terms of these performance measures is indicated, and an experimental study based on the selected method is conducted; the experiment involves testing the capability of a 3-RRR manipulator's end-effector in tracking the motions experienced by a human hip joint during normal gait cycles. Finally, the results of the experimental study indicate that the manipulator represents a feasible hip exoskeleton solution providing total kinematic compliance with the human hip joint's 3-degree-of-freedom motion capabilities.

A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials.

Falls are the leading cause of injury-related morbidity and mortality among older adults. Over 90 % of hip and wrist fractures and 60 % of traumatic brain injuries in older adults are due to falls. Another serious consequence of falls among older adults is the 'long lie' experienced by individuals who are unable to get up and remain on the ground for an extended period of time after a fall. Considerable research has been conducted over the past decade on the design of wearable sensor systems that can automatically detect falls and send an alert to care providers to reduce the frequency and severity of long lies. While most systems described to date incorporate threshold-based algorithms, machine learning algorithms may offer increased accuracy in detecting falls. In the current study, we compared the accuracy of these two approaches in detecting falls by conducting a comprehensive set of falling experiments with 10 young participants. Participants wore waist-mounted tri-axial accelerometers and simulated the most common causes of falls observed in older adults, along with near-falls and activities of daily living. The overall performance of five machine learning algorithms was greater than the performance of five threshold-based algorithms described in the literature, with support vector machines providing the highest combination of sensitivity and specificity.

Regression Model-Based Walking Speed Estimation Using Wrist-Worn Inertial Sensor.

Walking speed is widely used to study human health status. Wearable inertial measurement units (IMU) are promising tools for the ambulatory measurement of walking speed. Among wearable inertial sensors, the ones worn on the wrist, such as a watch or band, have relatively higher potential to be easily incorporated into daily lifestyle. Using the arm swing motion in walking, this paper proposes a regression model-based method for longitudinal walking speed estimation using a wrist-worn IMU. A novel kinematic variable is proposed, which finds the wrist acceleration in the principal axis (i.e. the direction of the arm swing). This variable (called pca-acc) is obtained by applying sensor fusion on IMU data to find the orientation followed by the use of principal component analysis. An experimental evaluation was performed on 15 healthy young subjects during free walking trials. The experimental results show that the use of the proposed pca-acc variable can significantly improve the walking speed estimation accuracy when compared to the use of raw acceleration information (p<0.01). When Gaussian process regression is used, the resulting walking speed estimation accuracy and precision is about 5.9% and 4.7%, respectively.