Objective: Various types of sensors, such as dynamometers, have been developed to assist in strengthening the pelvic floor muscles, aiming to improve the quality of life for women affected by urinary incontinence. This paper presents silicone encapsulation method for portable vaginal dynamometers that minimizes force measurement drift caused by thermal expansion mismatch between the silicone and the dynamometer housing due to body temperature. Methods: The encapsulation process involves two steps. First, based on the size and shape of the dynamometer, a mold is created to form a cured silicone sleeve slightly larger than the sensor. This sleeve is then slid over the dynamometer. A second silicone layer is subsequently applied over the sleeve and any exposed surfaces of the dynamometer. The dynamometers were tested in air and water at 40 °C to simulate thermal conditions and assess the force measurement drift at body temperature. Results: The proposed method limited the force drift to 0.014 N -a significant reduction compared to the 5 N observed when the silicone was directly applied to the dynamometer surface. This demonstrates the effectiveness of the two-layer encapsulation in mitigating the impact of thermal expansion on the measured force. Significance: This may pave the way to accurate personal pelvic dynamometers for at-home and personalized pelvic muscle training.

Intermittent pneumatic compression (IPC) systems apply external pressure to the lower limbs and enhance peripheral blood flow. We previously introduced a cardiac-gated compression system that enhanced arterial blood velocity (BV) in the lower limb compared to fixed compression timing (CT) for seated and standing subjects. However, these pilot studies found that the CT that maximized BV was not constant across individuals and could change over time. Current CT modelling methods for IPC are limited to predictions for a single day and one heartbeat ahead. However, IPC therapy for may span weeks or longer, the BV response to compression can vary with physiological state, and the best CT for eliciting the desired physiological outcome may change, even for the same individual. We propose that a deep reinforcement learning (DRL) algorithm can learn and adaptively modify CT to achieve a selected outcome using IPC. Herein, we target maximizing lower limb arterial BV as the desired outcome and build participant-specific simulated lower limb environments for 6 participants. We show that DRL can adaptively learn the CT for IPC that maximized arterial BV. Compared to previous work, the DRL agent achieves 98% ± 2 of the resultant blood flow and is faster at maximizing BV; the DRL agent can learn an "optimal" policy in 15 minutes ± 2 on average and can adapt on the fly. Given a desired objective, we posit that the proposed DRL agent can be implemented in IPC systems to rapidly learn the (potentially time-varying) "optimal" CT with a human-in-the-loop.

Intermittent pneumatic compression (IPC) systems are employed to treat vascular diseases. It has been shown that applying cardiac-gated compression effectively enhances femoral blood velocity (BV), but the optimal compression timing likely varies between individuals and may vary over time. While a previous work has shown the usability of one heartbeat ahead BV estimation to optimize the compression timing, that study was limited to a single treatment session and the BV estimator performance may deteriorate for the next sessions. Therefore, the goal of this study is to develop BV estimators and evaluate their accuracy over a longer time-scale. Six participants wore a custom IPC system and experienced random cardiac-gated compression timings for 1.5 hours per day for two days. A data- driven model was trained on electrocardiogram and applied pressure data to predict femoral BV one heartbeat ahead in a closed loop manner. The mean R2for this model across participants on the second session was 0.74 ± 0.09 and the mean absolute error was approximately 3%, which is a reduction of only 11% compared to the first sessions, for both metrics. This study is the first to show that BV across IPC sessions can be predicted using a pre-trained model. This work may lead to a significant improvement in IPC performance with only an initial model training session.

OBJECTIVE: To develop and evaluate an accurate method for cuffless blood pressure (BP) estimation during moderate- and heavy-intensity exercise. METHODS: Twelve participants performed three cycling exercises: a ramp-incremental exercise to exhaustion, and moderate and heavy pseudorandom binary sequence exercises on an electronically braked cycle ergometer over the course of 21 minutes. Subject-specific and population-based nonlinear autoregressive models with exogenous inputs (NARX) were compared with feedforward artificial neural network (ANN) models and pulse arrival time (PAT) models. RESULTS: Population-based NARX models, (applying leave-one-subject-out cross-validation), performed better than the other models and showed good capability for estimating large changes in mean arterial pressure (MAP). The models were unable to track consistent decreases in BP during prolonged exercise caused by reduction in peripheral vascular resistance, since this information is apparently not encoded in the employed proxy physiological signals (electrocardiography and forehead PPG) used for BP estimation. Nevertheless, the population-based NARX model had an error standard deviation of 11.0 mmHg during the entire exercise window, which improved to 9.0 mmHg when the model was periodically calibrated every 7 minutes. CONCLUSION: Population-based NARX models can estimate BP during moderate- and heavy-intensity exercise but need periodic calibration to account for the change in vascular resistance during exertion. SIGNIFICANCE: MAP can be continuously tracked during exercise using only wearable sensors, making monitoring exercise physiology more convenient and accessible.

The objective is to develop a cuffless method that accurately estimates blood pressure (BP) during activities of daily living. User-specific nonlinear autoregressive models with exogenous inputs (NARX) are implemented using artificial neural networks to estimate the BP waveforms from electrocardiography and photoplethysmography signals. To broaden the range of BP in the training data, subjects followed a short procedure consisting of sitting, standing, walking, Valsalva maneuvers, and static handgrip exercises. The procedure was performed before and after a six-hour testing phase wherein five participants went about their normal daily living activities. Data were further collected at a four-month time point for two participants and again at six months for one of the two. The performance of three different NARX models was compared with three pulse arrival time (PAT) models. The NARX models demonstrate superior accuracy and correlation with "ground truth" systolic and diastolic BP measures compared to the PAT models and a clear advantage in estimating the large range of BP. Preliminary results show that the NARX models can accurately estimate BP even months apart from the training. Preliminary testing suggests that it is robust against variabilities due to sensor placement. This establishes a method for cuffless BP estimation during activities of daily living that can be used for continuous monitoring and acute hypotension and hypertension detection.