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.

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.

An integrated microfluidic chip for immunomagnetic detection and isolation of rare prostate cancer cells from blood.

The quantitative and qualitative analysis of circulating tumor cells (CTCs) has the potential to improve the clinical management of several cancers, including prostate cancer. As such, there is much interest in the isolation of CTCs from the peripheral blood of cancer patients. We report the design, fabrication, and proof-of-principle testing of an integrated permalloy-based microfluidic chip for immunomagnetic isolation of blood-borne prostate cancer cells using an antibody targeting prostate surface membrane antigen (PSMA). The preliminary results using spiked blood samples indicate that the proposed device is consistently capable of isolating prostate cancer cells with high sensitivity (up to 98 %) at clinically relevant low concentrations (down to 20 cells/mL) and an acceptable throughput (100 µL/min).

Biometric system for measuring gait and fall characteristics captured on video.

This paper presents a methodology that quantifies gait and fall characteristics from video of real-life fall events. The method consists in selecting on-screen the points on the ground where the feet are in contact with the ground. The essence of the method lies in establishing a transformation from the video frames to the "real world." In projected images, geometric properties such as lengths, angles, and parallelism are not preserved; thus, concepts of projective geometry are applied, namely homography. Because the ground is an invariant plane, using this plane for homography results in a constant transformation. The homographic transformation relies on the accuracy in the selection of on-screen points. An optimization algorithm that minimizes the errors caused by inaccurate on-screen point selection improves the results of the homographic transformation. Experimental trials are conducted at three walking velocities (slow, preferred, and fast) using two video cameras and a GAITRite walkway system. Spatial parameters of two independent video analyses are compared with the GAITRite system, yielding a limit of agreement of step length from -2.12 cm to 2.03 cm. Temporal parameters are less confident due to the existence of dropped frames in the video footage. This method is then used to analyze two real fall events as demonstrative cases. First, the gait characteristics are analyzed before imbalance, and subsequently, the characteristics of stepping are analyzed during the fall. In particular, we propose the stepping/impact angle as the metric that quantifies how much stepping affected the direction of the fall.

Nonlinear Heart Rate Variability Analysis for Sleep Stage Classification Using Integration of Ballistocardiogram and Apple Watch.

Purpose: Wearable or non-contact, non-intrusive devices present a practical alternative to traditional polysomnography (PSG) for daily assessment of sleep quality. Physiological signals have been known to be nonlinear and nonstationary as the body adapts to states of rest or activity. By integrating more sophisticated nonlinear methodologies, the accuracy of sleep stage identification using such devices can be improved. This advancement enables individuals to monitor and adjust their sleep patterns more effectively without visiting sleep clinics. Patients and Methods: Six participants slept for three cycles of at least three hours each, wearing PSG as a reference, along with an Apple Watch, an actigraphy device, and a ballistocardiography (BCG) bed sensor. The physiological signals were processed with nonlinear methods and trained with a long short-term memory (LSTM) model to classify sleep stages. Nonlinear methods, such as return maps with advanced techniques to analyze the shape and asymmetry in physiological signals, were used to relate these signals to the autonomic nervous system (ANS). The changing dynamics of cardiac signals in restful or active states, regulated by the ANS, were associated with sleep stages and quality, which were measurable. Results: Approximately 73% agreement was obtained by comparing the combination of the BCG and Apple Watch signals against a PSG reference system to classify rapid eye movement (REM) and non-REM sleep stages. Conclusion: Utilizing nonlinear methods to evaluate cardiac dynamics showed an improved sleep quality detection with the non-intrusive devices in this study. A system of non-intrusive devices can provide a comprehensive outlook on health by regularly measuring sleeping patterns and quality over time, offering a relatively accessible method for participants. Additionally, a non-intrusive system can be integrated into a user's or clinic's bedroom environment to measure and evaluate sleep quality without negatively impacting sleep. Devices placed around the bedroom could measure user vitals over longer periods with minimal interaction from the user, representing their natural sleeping trends for more accurate health and sleep disorder diagnosis.

Theoretical analysis of the state of balance in bipedal walking.

This paper presents a theoretical analysis based on classic mechanical principles of balance of forces in bipedal walking. Theories on the state of balance have been proposed in the area of humanoid robotics and although the laws of classical mechanics are equivalent to both humans and humanoid robots, the resulting motion obtained with these theories is unnatural when compared to normal human gait. Humanoid robots are commonly controlled using the zero moment point (ZMP) with the condition that the ZMP cannot exit the foot-support area. This condition is derived from a physical model in which the biped must always walk under dynamically balanced conditions, making the centre of pressure (CoP) and the ZMP always coincident. On the contrary, humans follow a different strategy characterized by a 'controlled fall' at the end of the swing phase. In this paper, we present a thorough theoretical analysis of the state of balance and show that the ZMP can exit the support area, and its location is representative of the imbalance state characterized by the separation between the ZMP and the CoP. Since humans exhibit this behavior, we also present proof-of-concept results of a single subject walking on an instrumented treadmill at different speeds (from slow 0.7 m/s to fast 2.0 m/s walking with increments of 0.1 m/s) with the motion recorded using an optical motion tracking system. In order to evaluate the experimental results of this model, the coefficient of determination (R2) is used to correlate the measured ground reaction forces and the resultant of inertial and gravitational forces (anteroposterior R² = 0.93, mediolateral R² = 0.89, and vertical R² = 0.86) indicating that there is a high correlation between the measurements. The results suggest that the subject exhibits a complete dynamically balanced gait during slow speeds while experiencing a controlled fall (end of swing phase) with faster speeds. This is quantified with the root-mean-square deviation (RMSD) between the CoP and the ZMP, a relationship that grows exponentially, suggesting that the ZMP exits the support area earlier with faster walking speeds (relative to the stride duration). We conclude that the ZMP is a significant concept that can be exploited for the analysis of bipedal balance, but we also challenge the control strategy adopted in humanoid robotics that forces the ZMP to be contained within the support area causing the robot to follow unnatural patterns.

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.

Doppler ultrasound-based measurement of tendon velocity and displacement for application toward detecting user-intended motion.

The motivation of this research is to non-invasively monitor the wrist tendon's displacement and velocity, for purposes of controlling a prosthetic device. This feasibility study aims to determine if the proposed technique using Doppler ultrasound is able to accurately estimate the tendon's instantaneous velocity and displacement. This study is conducted with a tendon mimicking experiment consisting of two different materials: a commercial ultrasound scanner, and a reference linear motion stage set-up. Audio-based output signals are acquired from the ultrasound scanner, and are processed with our proposed Fourier technique to obtain the tendon's velocity and displacement estimates. We then compare our estimates to an external reference system, and also to the ultrasound scanner's own estimates based on its proprietary software. The proposed tendon motion estimation method has been shown to be repeatable, effective and accurate in comparison to the external reference system, and is generally more accurate than the scanner's own estimates. After establishing this feasibility study, future testing will include cadaver-based studies to test the technique on the human arm tendon anatomy, and later on live human test subjects in order to further refine the proposed method for the novel purpose of detecting user-intended tendon motion for controlling wearable prosthetic devices.

Estimation of Attitude and External Acceleration Using Inertial Sensor Measurement During Various Dynamic Conditions.

This paper proposes a Kalman filter-based attitude (i.e., roll and pitch) estimation algorithm using an inertial sensor composed of a triaxial accelerometer and a triaxial gyroscope. In particular, the proposed algorithm has been developed for accurate attitude estimation during dynamic conditions, in which external acceleration is present. Although external acceleration is the main source of the attitude estimation error and despite the need for its accurate estimation in many applications, this problem that can be critical for the attitude estimation has not been addressed explicitly in the literature. Accordingly, this paper addresses the combined estimation problem of the attitude and external acceleration. Experimental tests were conducted to verify the performance of the proposed algorithm in various dynamic condition settings and to provide further insight into the variations in the estimation accuracy. Furthermore, two different approaches for dealing with the estimation problem during dynamic conditions were compared, i.e., threshold-based switching approach versus acceleration model-based approach. Based on an external acceleration model, the proposed algorithm was capable of estimating accurate attitudes and external accelerations for short accelerated periods, showing its high effectiveness during short-term fast dynamic conditions. Contrariwise, when the testing condition involved prolonged high external accelerations, the proposed algorithm exhibited gradually increasing errors. However, as soon as the condition returned to static or quasi-static conditions, the algorithm was able to stabilize the estimation error, regaining its high estimation accuracy.

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.

A novel permalloy based magnetic single cell micro array.

Devices capable of automatically aligning cells onto geometrical arrays are of great interest to biomedical researchers. Such devices can facilitate the study of numerous cells while the cells remain physically separated from one another. In this way, cell arrays reduce cell-to-cell interactions while the cells are all subjected to common stimuli, which allows individual cell behaviour to be revealed. The use of arrays allows for the parallel analysis of single cells, facilitates data logging, and opens the door to the use of automated machine-based single cell analysis techniques. A novel permalloy based magnetic single cell micro array (MSCMA) is presented in this paper. The MSCMA creates an array of magnetic traps by generating magnetic flux density peaks at predefined locations. When using cells labelled with immunomagnetic labels, the cells will interact with the magnetic fields, and can be captured at the magnetic trap sites. Prototypes of the MSCMA have been successfully fabricated and tested using both fixed and live Jurkat cells (10 microm average diameter) that were labelled. The prototypes performed as predicted during experimental trials. The experimental results show that the MSCMA can randomly array up to 136 single cells per square mm. The results also show that the number of single cells captured is a function of the trap site density of the MSCMA design and the cell density in the fluid sample.

RoboSCell: an automated single cell arraying and analysis instrument.

Single cell research has the potential to revolutionize experimental methods in biomedical sciences and contribute to clinical practices. Recent studies suggest analysis of single cells reveals novel features of intracellular processes, cell-to-cell interactions and cell structure. The methods of single cell analysis require mechanical resolution and accuracy that is not possible using conventional techniques. Robotic instruments and novel microdevices can achieve higher throughput and repeatability; however, the development of such instrumentation is a formidable task. A void exists in the state-of-the-art for automated analysis of single cells. With the increase in interest in single cell analyses in stem cell and cancer research the ability to facilitate higher throughput and repeatable procedures is necessary. In this paper, a high-throughput, single cell microarray-based robotic instrument, called the RoboSCell, is described. The proposed instrument employs a partially transparent single cell microarray (SCM) integrated with a robotic biomanipulator for in vitro analyses of live single cells trapped at the array sites. Cells, labeled with immunomagnetic particles, are captured at the array sites by channeling magnetic fields through encapsulated permalloy channels in the SCM. The RoboSCell is capable of systematically scanning the captured cells temporarily immobilized at the array sites and using optical methods to repeatedly measure extracellular and intracellular characteristics over time. The instrument's capabilities are demonstrated by arraying human T lymphocytes and measuring the uptake dynamics of calcein acetoxymethylester--all in a fully automated fashion.

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.

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.

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.

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.