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Sequence time-domain reflectometry (STDR) and spread spectrum time-domain reflectometry (SSTDR) detect, locate, and diagnose faults in live (energized) electrical systems. In this paper, we survey the present SSTDR literature for discussions on theory, algorithms used in its analysis, and its more prominent implementations and applications. Our review includes both scientific litera-ture and selected patents. We also discuss future applications of SSTDR.

Hill-type muscle models are widely employed in simulations of human movement. Yet, the parameters underlying these models are difficult or impossible to measure in vivo. Prior studies demonstrate that Hill-type muscle parameters are encoded within dynamometric data. But, a generalizable approach for estimating these parameters from dynamometric data has not been realized. We aimed to leverage musculoskeletal models and artificial neural networks to classify one Hill-type muscle parameter (maximum isometric force) from easily measurable dynamometric data (simulated lateral pinch force). We tested two neural networks (feedforward and long short-term memory) to identify if accounting for dynamic behavior improved accuracy. We generated four datasets via forward dynamics, each with increasing complexity from adjustments to more muscles. Simulations were grouped and evaluated to show how varying the maximum isometric force of thumb muscles affects lateral pinch force. Both neural networks classified these groups from lateral pinch force alone. Both neural networks achieved accuracies above 80% for datasets which varied only the flexor pollicis longus and/or the abductor pollicis longus. The inclusion of muscles with redundant functions dropped model accuracies to below 30%. While both neural networks were consistently more accurate than random guess, the long short-term memory model was not consistently more accurate than the feedforward model. Our investigations demonstrate that artificial neural networks provide an inexpensive, data-driven approach for approximating Hill-type muscle-tendon parameters from easily measurable data. However, muscles of redundant function or of little impact to force production make parameter classification more challenging.

Few studies address guided wave structural health monitoring under controlled and dynamic environments, largely due to the lack of a public benchmark dataset. To address this gap, this paper presents a public dataset from a long-term outdoor structural monitoring experiment conducted at the University of Utah, Salt Lake City. The monitoring, spanning over 4.5 years, collected approximately 6.4 million guided waves under both regular environmental variations (e.g., daily temperature changes ranging from 260.95 K (−12.2 °C) to 325.65 K (52.5 °C)) and irregular variations (e.g., rain and snow). The measured guided waves in the public dataset are also affected by sensor drift and installation shifts consistently over time. Additionally, thirteen types of damage were introduced to the monitored structure to support damage detection and severity evaluation under these conditions. The dataset includes measurement times, temperature, humidity, air pressure, brightness, and weather information to aid in damage detection. The provided public dataset aims to assist researchers in developing more practical methods for structural health monitoring in uncontrolled and dynamic environments.

Developing musculoskeletal hand models requires a variety of experimental biomechanics data. However, collecting robust biomechanics hand data is a time intensive process leading to a lack of widely available datasets. To address this issue the biomechanics hand modeling database (BHaM) was made as a collection of experimental data to aid the development, testing, and validation of musculoskeletal models and simulations. BHaM includes two datasets: (1) a population dataset (n = 726 adults) describing hand strength (pinch and grip), self-reported hand function (Michigan Hand Questionnaire), and anthropometric measurements (from photographs), and (2) a biomechanics dataset (n = 30 adults) describing kinematics (marker-based motion capture), kinetics (isometric and isokinetic data), and electromyography (surface and fine wire) during 19 tasks across the elbow, wrist, and hand. A subset of the biomechanics dataset (n = 15 adults) also includes magnetic resonance imaging of the shoulder through wrist. Participants for both datasets were recruited to represent a diverse population of healthy adults, ranging from 18 to 91 years.

Obtaining large biomechanical datasets for machine learning is an ongoing challenge. Physics-based simulations offer one approach for generating large datasets, but many simulation methods, such as computed muscle control (CMC), are computationally costly. In contrast, interpolation methods, such as inverse distance weighting (IDW), are computationally fast. We examined whether IDW is a low-cost and accurate approach for interpolating muscle activations from CMC.IDW was evaluated using lateral pinch simulations in OpenSim. Simulated pinch data were organized into grids of varying sparsity (high, medium, and low density), where each grid point represented the muscle activations associated with a unique combination of mass and height of a young adult. For each grid, muscle activations were calculated via CMC and IDW for 108 random mass-height pairs that were not coincident with simulation grid vertices. We evaluated the interpolation errors from IDW for each grid, as well as the sensitivity of lateral pinch force to these errors. The root mean square error (RMSE) associated with interpolated muscle activations decreased with increasing grid density and never exceeded 4%. While CMC received a target thumb-tip force of 40 N, errors from the interpolated muscle activations never impacted the simulated force magnitude by more than 0.1 N. Furthermore, the computation time for CMC simulations averaged 4.22 core-minutes, while IDW averaged 0.95 core-seconds per mass-height pair.These results indicate IDW is a practical approach for rapidly estimating muscle activations from sparse CMC datasets. Future works could adapt our IDW approach to evaluate other tasks, biomechanical features, and/or populations.

As datasets increase in size and complexity, biomechanists have turned to artificial intelligence (AI) to aid their analyses. This paper explores how explainable AI (XAI) can enhance the interpretability of biomechanics data derived from musculoskeletal simulations. We use machine learning to classify the simulated lateral pinch data as belonging to models with healthy or one of two types of surgically altered wrists. This simulation-based classification task is analogous to using biomechanical movement and force data to clinically diagnose a pathological state. The XAI describes which musculoskeletal features best explain the classifications and, in turn, the pathological states, at both the local (individual decision) level and global (entire algorithm) level. We demonstrate that these descriptions agree with assessments in the literature and additionally identify the blind spots that can be missed with traditional statistical techniques.

Subject-specific musculoskeletal models are a promising avenue for personalized healthcare. However, current methods for producing personalized models require dense, biomechanical datasets that include expensive and time-consuming physiological measurements. For personalized models to be clinically useful, we must be able to rapidly generate models from simple, easy to collect data. In this context, the objective of this paper is to evaluate if and how simple data, namely height/weight and pinch force data, can be used to achieve model personalization via machine learning. Using simulated lateral pinch force measurements from a synthetic population of 40,000 randomly generated subjects, we train neural networks to estimate four Hill-type muscle model parameters and bone density. We compare parameter estimates to the true parameters of 10,000 additional synthetic subjects. We also generate new personalized models using the parameter estimates and perform new lateral pinch simulations to compare predicted forces using these personalized models to those generated using a baseline model. We demonstrate that increasing force measurement complexity reduces the root-mean-square error in the majority of parameter estimates. Additionally, musculoskeletal models using neural network-based parameter estimates provide up to an 80% reduction in absolute error in simulated forces when compared to a generic model. Thus, easily obtained force measurements may be suitable for personalizing models of the thumb, although extending the method to more tasks and models involving other joints likely requires additional measurements.

Biomechanics studies rely on non-random recruitment methods to obtain study participants. However, the use of common recruitment methods and small sample sizes may influence a given study’s generalizability due to selection bias. To improve generalizability, ecological validity, and participant convenience, recent biomechanics studies have moved beyond lab conditions. However, it is unknown if simply leaving the lab space and increasing sample sizes reduces the risks associated with selection bias. Previous studies relied on chart and literature reviews to identify selection bias. Herein, we build upon this work by exploring the potential for and influence of selection bias in three common recruiting methods by performing an experimental, population-level study on hand biomechanics. Hand biomechanics was assessed in the community using a portable lab setup to measure hand function, grip strength, and pinch strength. A total of 642 apparently healthy participants were recruited across 18 locations, with 426 participants selected based on complete data responses and being between the ages of 18 to 35. Sex stratified analysis was performed to see how recruiting only biomechanists, undergraduate students, or university affiliates changed population estimates of hand strength. The presence of selection bias was observed in all three test cases with both male and female biomechanists, graduate students, and non-university affiliates having significant increases in pinch and grip strengths ranging from 6.2% to 19.4% above overall population values. This study quantitively shows how simply leaving the lab and increasing subject recruitment does not eliminate the potential for selection bias in studies of hand biomechanics.

This paper studies a computationally efficient algebraic graph theory engine for simulating time-domain one-dimensional waves in a multi-segment transmission line, such as for reflectometry applications. Efficient simulation of time-domain signals in multi-segment transmission lines is challenging because the number of propagation paths (and therefore the number of operations) increases exponentially with each new interface. We address this challenge through the use of a frequencydomain, algebraic graphical model of wave propagation, which is then converted to the time domain via the Fourier transform. We use this model to achieve an exact, stable, and computationally efficient (O(NQ), where N is the number of segments and Q is the bandwidth) approach for studying one-dimensional wave propagation. Our approach requires the reflection and transmission coefficients for each interface and each segment's complex propagation constant. We compare our simulation results with known analytical solutions.

This paper studies the detection of hidden polymer matrix composite delaminations with a pitch–catch ultrasonic testing system and an agglomerative clustering algorithm. Existing ultrasonic testing methods characterize damage through normal-incidence pulse-echo measurements. Yet, these pulse-echo methods are ineffective at detecting delaminations underneath other delaminations. The ultrasonic waves only reflect off the top delamination. As a result, no information about the lower delamination is transmitted to the receiver. To address this problem, we investigate an oblique-angle pitch–catch ultrasonic testing method to transmit ultrasonic waves underneath the delaminations. The ultrasonic waves interact with the lower delaminations and carry that information to the receiver. We describe and discuss our experiments, which use two polytetrafluoro-ethylene (PTFE) inserts to simulate delaminations. We show that applying agglomerative clustering to the experimental data can successfully distinguish three regions: regions with two PTFE inserts, regions with only an upper PTFE insert, and regions with only the lower PTFE insert. Normal-incidence measurements only observe two regions.