In this study, we proposed a triboelectric nanogenerator (TENG) as a pressure sensor to measure the load imbalance on the tibial tray. To detect the load imbalance, we proposed a segmented quadrant design. The TENG pressure sensors with various micro-patterns, including pyramid, cylindrical, and bar patterns, are utilized to measure the axial forces with different sensitivity in different quadrants of the tibial tray. The functionality of the instrumented implant is examined through experimental testing on the package, evaluating its sensing capabilities and power harvesting. The relationship between each quadrant output and the axial force is determined, which enables characterizing the self-powered sensor performance. This relationship is then used to find the center of pressure, which is an important parameter for implant design. The detection of a shift in the center of pressure can be an early indication of loosening, which is one of the major causes of knee implant failure. In addition, we investigated the apparent power captured in resistance loads by applying a sinusoidal cyclic loading to the package harvester. Under an average walking load of 2200 N, each quadrant of the harvester-package prototype produces an apparent power of approximately at 1 Hz and at 2 Hz.

In this paper, with the goal of addressing the lack of tactile feedback in colorectal cancer (CRC) polyps diagnosis using a colonoscopy procedure, we propose the design and fabrication of a novel soft and inflatable strain-based tactile sensing balloon (SI-STSB). The proposed soft sensor features a unique stretchable sensing layer - that utilizes a liquid metal injected within spiral-shape microchannels of a stretchable substrate - and is integrated with a unique inflatable balloon mechanism. The proposed SI-STSB has been thoroughly characterized through different calibration experiments. Results demonstrate a phenomenal adjustable sensitivity with low hysteresis behavior under different experimental conditions for this sensor making it a great candidate for enhancing the existing diagnosis procedures.

Needle insertion using flexible bevel tip needles are a common minimally-invasive surgical technique for prostate cancer interventions. Flexible, asymmetric bevel tip needles enable physicians for complex needle steering techniques to avoid sensitive anatomical structures during needle insertion. For accurate placement of the needle, predicting the trajectory of these needles intra-operatively would greatly reduce the need for frequently needle reinsertions thus improving patient comfort and positive outcomes. However, predicting the trajectory of the needle during insertion is a complex task that has yet to be solved due to random needle-tissue interactions. In this paper, we present and validate for the first time a hybrid deep learning and model-based approach to handle the intra-operative needle shape prediction problem through, leveraging a validated Lie-group theoretic model for needle shape representation. Furthermore, we present a novel self-supervised learning and method in conjunction with the Lie-group shape model for training these networks in the absence of data, enabling further refinement of these networks with transfer learning. Needle shape prediction was performed in single-layer and double-layer homogeneous phantom tissue for C- and S-shape needle insertions. Our method demonstrates an average root-mean-square prediction error of 1.03 mm over a dataset containing approximately 3,000 prediction samples with maximum prediction steps of 110 mm.

Direct conversion X-ray detectors offer high spatial resolution and improved sensitivity over indirect conversion detectors. As interest increases in their applications, including utilization as the top layer in dual-layer detectors for polyenergetic X-ray detection, additional studies on fabrication techniques are required. Amorphous selenium is a well-studied high-Z semiconductor capable of high-resolution and high-sensitivity imaging in direct architectures. It is already commercially available for mammography, and much work has gone into developing it for higher-energy applications (>20 keV.) To fully attenuate energies required for the low energy (<35 keV) top layer in dual-layer detectors, a-Se thicknesses greater than 200 μm must be fabricated. However, evaporation crucibles have limited capacity and require reloading of crucible material to achieve higher thicknesses, which is performed by opening the chamber and breaking vacuum mid-fabrication. We investigate the effects of splitting the fabrication into two depositions - exposing the sample to air in between - on device performance. We find that there is no significant effect on the transport properties, and a small range of performance parameters can be found - suggesting small fluctuations between devices of ± 5%, independent of fabrication technique and thickness. This implies that as we increase our thicknesses to those required for the low-energy layer of the dual-layer detector, we can expect performance to be maintained.

Joint acoustic emissions (JAEs) have been used as a non-invasive sensing modality of joint health for different conditions such as acute injuries, osteoarthritis (OA), and rheumatoid arthritis (RA). Recent hardware improvements for sensing JAEs have made at-home sensing to supplement clinical visits a possibility. To complement these advances, models must be improved for JAEs to function as generalizable predictors of joint health. Addressing this need, this work investigates the effects of recording setup, location-specific factors, and participant population on previously validated JAE models. The effect of recording setup is first investigated by testing a model developed previously for a wearable brace to predict erythrocyte sedimentation rate (ESR) in participants with RA on benchtop data, resulting in an area under the receiver-operating characteristic curve (AUC), sensitivity, and specificity of 0.79, 0.73, and 0.81 respectively. Investigating the effects of participant population type and location-specific factors, a feature-based model and a convolutional neural network (CNN) were both trained with healthy and RA data to predict ESR level, and then tested on a new dataset containing healthy, pre-radiographic osteoarthritis (Pre-OA), and OA data. The feature-based model had an AUC of 0.69 and 0.94, a sensitivity of 0.38 and 0.80, and a sensitivity of 1, while the CNN had an AUC of 0.85 and 0.99, a sensitivity of 0.50 and 1, and a specificity of 0.90 for detecting Pre-OA and OA respectively. The ability to generalize models across setup, location, and participant population provides a foundation for using JAEs as a measure of joint health.

We developed an ankle-worn gait monitoring system for tracking gait parameters, including length, width, and height. The system utilizes ankle bracelets equipped with wide-angle infrared (IR) stereo cameras tasked with monitoring a marker on the opposing ankle. A computer vision algorithm we have also developed processes the imaged marker positions to estimate the length, width, and height of the person's gait. Through testing on multiple participants, the prototype of the proposed gait monitoring system exhibited notable performance, achieving an average accuracy of 96.52%, 94.46%, and 95.29% for gait length, width, and height measurements, respectively, despite distorted wide-angle images. The OptiGait system offers a cost-effective and user-friendly alternative compared to existing gait parameter sensing systems, delivering comparable accuracy in measuring gait length and width. Notably, the system demonstrates a novel capability in measuring gait height, a feature not previously reported in the literature.

Continuous monitoring of bladder activity during normal daily activities would improve clinical diagnostics and understanding of the mechanisms underlying bladder function, or help validate how differing neuromodulation strategies affect the bladder. This work describes a urological monitor of conscious activity (UroMOCA). The UroMOCA included a pressure sensor, urine impedance-sensing electrodes, and wireless battery recharge and data transmission circuitry. Components were assembled on a circuit board and encapsulated with an epoxy/silicone molded package that allowed Pt-Ir electrode feedthrough for urine contact. Packaged UroMOCAs measured 12 × 18 × 6 mm. UroMOCAs continuously transmitted data from all onboard sensors at 10 Hz at 30 cm range, and ran for up to 44 hours between wireless recharges. After calibration, implantations were performed in 11 animals. Animals carried the device for 28 days, enabling many observations of bladder behavior during natural, conscious behavior. testing confirmed the UroMOCA did not impact bladder function after a two-week healing period. Pressure data were highly correlated to a reference catheter used during an anesthetized follow-up. Static volume sensor data were less accurate, but demonstrated reliable detection of bladder volume decreases, and distinguished between voiding and non-voiding bladder events.

In this paper, we propose mmPose-FK, a novel millimeter wave (mmWave) radar-based pose estimation method that employs a dynamic forward kinematics (FK) approach to address the challenges posed by low resolution, specularity, and noise artifacts commonly associated with mmWave radars. These issues often result in unstable joint poses that vibrate over time, reducing the effectiveness of traditional pose estimation techniques. To overcome these limitations, we integrate the FK mechanism into the deep learning model and develop an end-to-end solution driven by data. Our comprehensive experiments using various matrices and benchmarks highlight the superior performance of mmPose-FK, especially when compared to our previous research methods. The proposed method provides more accurate pose estimation and ensures increased stability and consistency, which underscores the continuous improvement of our methodology, showcasing superior capabilities over its antecedents. Moreover, the model can output joint rotations and human bone lengths, which could be further utilized for various applications such as gait parameter analysis and height estimation. This makes mmPose-FK a highly promising solution for a wide range of applications in the field of human pose estimation and beyond.

Photoacoustic (PA) imaging provides deep tissue molecular imaging of chromophores with optical absorption contrast and ultrasonic resolution. Present PA imaging techniques are predominantly limited to one 2D plane per acquisition. 2D ultrasound transducers, required for real-time 3D PA imaging, are high-cost, complex to fabricate and have limited scalability in design. We present novel PCB-based 2D matrix ultrasound transducer arrays that are capable of being bulk manufactured at low-cost without using laborious ultrasound fabrication tools. The 2D ultrasound array specifications are easily scalable with respect to widely available PCB design and fabrication tools at low cost. To demonstrate scalability, we fabricated low (11 MHz) frequency 8x8 matrix array and high (40 MHz) frequency 4x4 matrix array by directly bonding an undiced polyvinylidene fluoride (PVDF) piezoelectric material of desired thickness to the custom designed PCB substrate. Characterization results demonstrate wideband PA receive sensitivity for both low (87%) and high (188%) frequency arrays. Volumetric PA imaging results of light absorbing targets inside optical scattering medium demonstrate improved spatial resolution and field of view with increase in aperture size.

Ultra high frequency (UHF) passive radio frequency identification (RFID) tag-based sensors are proposed for intravenous (IV) fluid level monitoring in medical Internet of Things (IoT) applications. Two versions of the sensor are proposed: a binary sensor (i.e., full vs. empty state sensing) and a real-time (., continuous level) sensor. The operating principle is demonstrated using full-wave electromagnetic simulation at 910 MHz and validated with experimental results. Generalized Additive Model (GAM) and random forest algorithms are employed for each interrogation dataset. Real-time sensing is accomplished with small deviations across the models. A minimum of 72% and a maximum of 97% of cases are within a 20% error for the GAM model and 62% to 98% for the random forest model. The proposed sensor is battery-free, lightweight, low-cost, and highly reliable. The read range of the proposed sensor is 4.6 m.

Chronic wounds have emerged as a significant healthcare burden, affecting millions of patients worldwide and presenting a substantial challenge to healthcare systems. The diagnosis and management of chronic wounds are notably intricate, with inappropriate management contributing significantly to the amputation of limbs. In this work, we propose a compact, wireless, battery-free, and multimodal wound monitoring system to facilitate timely and effective wound treatment. The design of this monitoring system draws on the principles of higher-order parity-time symmetry, which incorporates spatially balanced gain, neutral, and loss, embodied by an active - reader, an intermediator, and a passive sensor, respectively. Our experimental results demonstrate that this wireless wound sensor can detect temperature (T), relative humidity (RH), pressure (P), and pH with exceptional sensitivity and robustness, which are critical biomarkers for assessing wound healing status. Our experiments further validate the reliable sensing performance of the wound sensor on human skin and fish. This multifunctional monitoring system may provide a promising solution for the development of futuristic wearable sensors and integrated biomedical microsystems.

Consuming excessive amounts of alcohol causes impaired mobility and judgment and driving accidents, resulting in more than 800 injuries and fatalities each day. Passive methods to detect intoxicated drivers beyond the safe driving limit can facilitate Just-In-Time alerts and reduce Driving Under the Influence (DUI) incidents. Popularly-owned smartphones are not only equipped with motion sensors (accelerometer and gyroscope) that can be employed for passively collecting gait (walk) data but also have the processing power to run computationally expensive machine learning models. In this paper, we advance the state-of-the-art by proposing a novel method that utilizes a Bi-linear Convolution Neural Network (BiCNN) for analyzing smartphone accelerometer and gyroscope data to determine whether a smartphone user is over the legal driving limit (0.08) from their gait. After segmenting the gait data into steps, we converted the smartphone motion sensor data to a Gramian Angular Field (GAF) image and then leveraged the BiCNN architecture for intoxication classification. Distinguishing GAF-encoded images of the gait of intoxicated vs. sober users is challenging as the differences between the classes (intoxicated vs. sober) are subtle, also known as a fine-grained image classification problem. The BiCNN neural network has previously produced state-of-the-art results on fine-grained image classification of natural images. To the best of our knowledge, our work is the first to innovatively utilize the BiCNN to classify GAF encoded images of smartphone gait data in order to detect intoxication. Prior work had explored using the BiCNN to classify natural images or explored other gait-related tasks but not intoxication Our complete intoxication classification pipeline consists of several important pre-processing steps carefully adapted to the BAC classification task, including step detection and segmentation, data normalization to account for inter-subject variability, data fusion, GAF image generation from time-series data, and a BiCNN classification model. In rigorous evaluation, our BiCNN model achieves an accuracy of 83.5%, outperforming the previous state-of-the-art and demonstrating the feasibility of our approach.

The rapid advancement of biosensor technology has revolutionized healthcare, offering improved sensitivity, specificity, and portability. We have developed an optical cavity-based biosensor (OCB) as a promising solution due to its label-free detection, high sensitivity, real-time monitoring, multiplexing capability, and versatility. The OCB consists of an optical cavity structure (OCS), optical components, and a low-cost camera. The OCS is created by two partially reflective surfaces separated by a small gap, where the interaction between target analytes and immobilized receptors leads to a shift in the resonance transmission spectrum, caused by minute changes in the local refractive index (RI). In our previous work, we successfully detected these small changes with a simple OCS and cost-effective components using a differential detection method. Building upon these achievements, this study focuses on optimizing the OCS, improving the camera settings, and enhancing the differential detection approach. By increasing the reflectance of the surfaces and optimizing the optical cavity widths correspondingly, we achieved an improved limit of detection (LOD). We also investigated how the charge-coupled device (CCD) camera shutter time affects the LOD. Additionally, we introduced a new differential equation to further enhance the sensitivity of our system. Through these advancements, we could improve the LOD of the OCB by 7.2 times, specifically for an OCS with a cavity thickness of 9.881 m and a silver thickness of 46.87 nm. These findings not only contribute to the ongoing effort of optimizing the OCB, but also pave the way for the development of advanced point-of-care biosensors with enhanced detection capabilities.

Body motion tracking for medical applications has the potential to improve quality of life for people with physical or speech motor disorders. Current solutions available in the market are either inaccurate, not affordable, or are impractical for a medical setting or at home. Magnetic localization can address these issues thanks to its high accuracy, simplicity of use, wearability, and use of inexpensive sensors such as magnetometers. However, sources of unreliability affect magnetometers to such an extent that the localization model trained in a controlled environment might exhibit poor tracking accuracy when deployed to end users. Traditional magnetic calibration methods, such as ellipsoid fit (EF), do not sufficiently attenuate the effect of these sources of unreliability to reach a positional accuracy that is both consistent and satisfactory for our target applications. To improve reliability, we developed a calibration method called (PDIST) that reduces the distribution shift in the magnetic measurements between model training and deployment. In this paper, we focused on change in magnetization or magnetometer as sources of unreliability. Our results show that PDIST performs better than EF in decreasing positional errors by a factor of ~3 when magnetization is distorted, and up to ~7 when our localization model is tested on a different magnetometer than the one it was trained with. Furthermore, PDIST is shown to perform reliably by providing consistent results across all our data collection that tested various combinations of the sources of unreliability.

This study was undertaken to determine if knee acoustic emissions (KAE) measured at the point of care with a wearable device can classify knees with pre-radiographic osteoarthritis (pre-OA) from healthy knees. We performed a single-center cross-sectional observational study comparing KAE in healthy knees to knees with clinical symptoms compatible with knee OA that did not meet classification criteria for radiographic knee OA. KAE were measured during scripted maneuvers performed in clinic exam rooms or similarly noisy medical center locations in healthy (n=20), pre-OA (n=11), and, for comparison, OA (n=12) knees. Acoustic features were extracted from the KAE and used to train models to classify pre-OA, OA, and control knees with logistic regression. Model performance was measured and optimized with Leave-One-Out Cross-Validation. Regressive sensitivity analysis was performed to combine acoustic information from individual maneuvers to further optimize performance. Test-retest reliability of KAE was measured with intraclass correlation analysis. Classification models trained with KAE were accurate for both pre-OA and OA (94% accurate, 0.96 and 0.99 area under a receiver operating characteristic curve (AUC), respectively). Acoustic features selected for use in the optimized models had high test-retest reliability by intrasession and intersession intraclass correlation analysis (mean intraclass correlation coefficient 0.971 +/- 0.08 standard deviation). Analysis of KAE measured in acoustically uncontrolled medical settings using an easily accessible wearable device accurately classified pre-OA knees from healthy control knees in our small cohort. Accessible methods of identifying pre-OA could enable regular joint health monitoring and improve OA treatment and rehabilitation outcomes.

Conventional electromyography (EMG) measures the continuous neural activity during muscle contraction, but lacks explicit quantification of the actual contraction. Mechanomyography (MMG) and accelerometers only measure body surface motion, while ultrasound, CT-scan and MRI are restricted to in-clinic snapshots. Here we propose a novel radiomyography (RMG) for continuous muscle actuation sensing that can be wearable or touchless, capturing both superficial and deep muscle groups. We verified RMG experimentally by a wearable forearm sensor for hand gesture recognition (HGR). We first converted the sensor outputs to the time-frequency spectrogram, and then employed the vision transformer (ViT) deep learning network as the classification model, which can recognize 23 gestures with an average accuracy up to 99% on 8 subjects. By transfer learning, high adaptivity to user difference and sensor variation were achieved at an average accuracy up to 97%. We further extended RMG to monitor eye and leg muscles and achieved high accuracy for eye movement and body posture tracking. RMG can be used with synchronous EMG to derive stimulation-actuation waveforms for many potential applications in kinesiology, physiotherapy, rehabilitation, and human-machine interface.

For the last 20 years, researchers have developed accelerometers to function as ossicular vibration sensors in order to eliminate the external components of hearing aid and cochlear implant systems. To date, no accelerometer has met all of the stringent performance requirements necessary to function in this capacity. In this work, we present an accelerometer design with an equivalent noise floor less than 20 phon equal-loudness-level over a 0.1-8 kHz bandwidth in a package small enough to be implanted in the middle ear. Our approach uses a dual-bandwidth (two sensing elements) microelectromechanical systems piezoelectric accelerometer, sized using an area-minimization process based on an experimentally-validated analytical model of the sensor. The resulting bandwidth of the low-frequency sensing element is 0.1-1.25 kHz and that of the high-frequency sensing element is 1.25-8 kHz. These sensing elements fit within a silicon frame that is 795 μm × 778 μm, which can reasonably be housed along with a required integrated circuit in a 2.2 mm × 2.7 mm × 1 mm package. The estimated total mass of the packaged system is approximately 14 mg. This dual-bandwidth MEMS sensor fills a technological gap in current completely implantable auditory prosthesis research and development by enabling a device capable of meeting physical and performance specifications needed for use in the middle ear.

Due to their high sensitivity and selectivity, low cost, and good compatibility for sensor array integration, colorimetric gas sensors are widely used in hazardous gas detection, food freshness assessment, and gaseous biomarker identification. However, colorimetric gas sensors are usually designed for one-time discrete measurement because the sensing materials are entirely exposed to analytes during the sensing process. The fast consumption of sensing materials limits colorimetric sensors' applications in continuous analytes monitoring, increases the operation complexity and brings challenges for calibration. In this work, we reported a novel sensor design to prolong the lifetime of colorimetric gas sensors by engineering the gas diffusion process to preserve the sensing materials. We compared two geometries for gas diffusion control in a sensing matrix through simulation and experiment on an ammonia sensing platform. We found that the 2-dimensional gas diffusion geometry enabled a better sensor performance, including more stable and higher sensitivity and a more linear response to ammonia concentration compared to 1-dimensional gas diffusion geometry. We also demonstrated the usability of this diffusion-modulated colorimetric sensor for continuous environmental ammonia monitoring.

Continuum dexterous manipulators (CDMs) are suitable for performing tasks in a constrained environment due to their high dexterity and maneuverability. Despite the inherent advantages of CDMs in minimally invasive surgery, real-time control of CDMs' shape during nonconstant curvature bending is still challenging. This study presents a novel approach for the design and fabrication of a large deflection fiber Bragg grating (FBG) shape sensor embedded within the lumens inside the walls of a CDM with a large instrument channel. The shape sensor consisted of two fibers, each with three FBG nodes. A shape-sensing model was introduced to reconstruct the centerline of the CDM based on FBG wavelengths. Different experiments, including shape sensor tests and CDM shape reconstruction tests, were conducted to assess the overall accuracy of the shape-sensing. The FBG sensor evaluation results revealed the linear curvature-wavelength relationship with the large curvature detection of 0.045 mm and a high wavelength shift of up to 5.50 nm at a 90° bending angle in both the bending directions. The CDM's shape reconstruction experiments in a free environment demonstrated the shape-tracking accuracy of 0.216 ± 0.126 mm for positive/negative deflections. Also, the CDM shape reconstruction error for three cases of bending with obstacles was observed to be 0.436 ± 0.370 mm for the proximal case, 0.485 ± 0.418 mm for the middle case, and 0.312 ± 0.261 mm for the distal case. This study indicates the adequate performance of the FBG sensor and the effectiveness of the model for tracking the shape of the large-deflection CDM with nonconstant-curvature bending for minimally invasive orthopedic applications.

Many prevalent heart diseases can be indicated by the features of the jugular venous pulse (JVP), an efficacious indicator of right heart health. However, JVP dynamics are not widely utilized in clinical settings as its observation and sensing remain cumbersome. Non-invasive measures of cardiac behavior, including the JVP, are of growing interest to enable continuous and at-home monitoring of cardiac disorders. In this work, we propose a wearable near-field radio-frequency (RF) sensor affixed with a neck collar on the clavicle over the internal jugular vein to enable non-invasive JVP sensing. We employed a complex vector injection signal processing method to extract repeatable JVP waveform features in multiple postures. With a 21-subject human study, we demonstrated morphologically consistent JVP sensing with consistent a-, c-, and v-wave feature timings, benchmarked by synchronous electrocardiogram and phonocardiogram. Further, inter-postural experiments demonstrated the capability of the proposed system to quantify morphological changes to the JVP which are present in many cardiac disorders. The results of this work suggest the proposed near-field RF sensor is capable of non-invasive JVP monitoring, potentially enabling improved sensing in both clinical and ambulatory environments.

Concern about falling is prevalent in older population. This condition would cause a series of adverse physical and psychological consequences for older adults' health. Traditional assessment of concern about falling is relied on self-reported questionnaires and thus is too subjective. Therefore, we proposed a novel multi-time-scale topic modelling approach to quantitatively evaluate concern about falling by analyzing triaxial acceleration signals collected from a wearable pendent sensor. Different posture segments were firstly recognized to extract their corresponding feature subsets. Then, each selected feature related to concern about falling was clustered into discrete levels as feature letters of artificial words in different time scales. As a result, all older participants' signal recordings were converted to a collection of artificial documents, which can be processed by natural language processing methodologies. The topic modelling technique was used to discover daily posture behavior patterns from these documents as discriminants between older adults with different levels of concern about falling. The results indicated that there were significant differences in distributions of posture topics between groups of older adults with different levels of concern about falling. Additionally, the transitions of posture topics over daytime and nighttime revealed temporal regularities of posture behavior patterns of older adult's active and inactive status, which were substantially different for older adults with different levels of concern about falling. Finally, the level of concern about falling was accurately determined with accuracy of 71.2% based on the distributions of posture topics combined with the mobility performance metrics of walking behaviors and demographic information.