Design and Fabrication of Embroidered Textile Strain Sensors: An Alternative to Stitch-Based Strain Sensors.

Smart textile sensors have been gaining popularity as alternative methods for the continuous monitoring of human motion. Multiple methods of fabrication for these textile sensors have been proposed, but the simpler ones include stitching or embroidering the conductive thread onto an elastic fabric to create a strain sensor. Although multiple studies have demonstrated the efficacy of textile sensors using the stitching technique, there is almost little to no information regarding the fabrication of textile strain sensors using the embroidery method. In this paper, a design guide for the fabrication of an embroidered resistive textile strain sensor is presented. All of the required design steps are explained, as well as the different embroidery design parameters and their optimal values. Finally, three embroidered textile strain sensors were created using these design steps. These sensors are based on the principle of superposition and were fabricated using a stainless-steel conductive thread embroidered onto a polyester-rubber elastic knit structure. The three sensors demonstrated an average gauge factor of 1.88±0.51 over a 26% working range, low hysteresis (8.54±2.66%), and good repeatability after being pre-stretched over a certain number of stretching cycles.

User-Independent Hand Gesture Recognition Classification Models Using Sensor Fusion.

Recently, it has been proven that targeting motor impairments as early as possible while using wearable mechatronic devices for assisted therapy can improve rehabilitation outcomes. However, despite the advanced progress on control methods for wearable mechatronic devices, the need for a more natural interface that allows for better control remains. To address this issue, electromyography (EMG)-based gesture recognition systems have been studied as a potential solution for human-machine interface applications. Recent studies have focused on developing user-independent gesture recognition interfaces to reduce calibration times for new users. Unfortunately, given the stochastic nature of EMG signals, the performance of these interfaces is negatively impacted. To address this issue, this work presents a user-independent gesture classification method based on a sensor fusion technique that combines EMG data and inertial measurement unit (IMU) data. The Myo Armband was used to measure muscle activity and motion data from healthy subjects. Participants were asked to perform seven types of gestures in four different arm positions while using the Myo on their dominant limb. Data obtained from 22 participants were used to classify the gestures using three different classification methods. Overall, average classification accuracies in the range of 67.5-84.6% were obtained, with the Adaptive Least-Squares Support Vector Machine model obtaining accuracies as high as 92.9%. These results suggest that by using the proposed sensor fusion approach, it is possible to achieve a more natural interface that allows better control of wearable mechatronic devices during robot assisted therapies.

Implementing Machine Learning Algorithms to Classify Postures and Forecast Motions When Using a Dynamic Chair.

Many modern jobs require long periods of sitting on a chair that may result in serious health complications. Dynamic chairs are proposed as alternatives to the traditional sitting chairs; however, previous studies have suggested that most users are not aware of their postures and do not take advantage of the increased range of motion offered by the dynamic chairs. Building a system that identifies users' postures in real time, as well as forecasts the next few postures, can bring awareness to the sitting behavior of each user. In this study, machine learning algorithms have been implemented to automatically classify users' postures and forecast their next motions. The random forest, gradient decision tree, and support vector machine algorithms were used to classify postures. The evaluation of the trained classifiers indicated that they could successfully identify users' postures with an accuracy above 90%. The algorithm can provide users with an accurate report of their sitting habits. A 1D-convolutional-LSTM network has also been implemented to forecast users' future postures based on their previous motions, the model can forecast a user's motions with high accuracy (97%). The ability of the algorithm to forecast future postures could be used to suggest alternative postures as needed.

Using Deep Learning for Task and Tremor Type Classification in People with Parkinson's Disease.

Hand tremor is one of the dominating symptoms of Parkinson's disease (PD), which significantly limits activities of daily living. Along with medications, wearable devices have been proposed to suppress tremor. However, suppressing tremor without interfering with voluntary motion remains challenging and improvements are needed. The main goal of this work was to design algorithms for the automatic identification of the tremor type and voluntary motions, using only surface electromyography (sEMG) data. Towards this goal, a bidirectional long short-term memory (BiLSTM) algorithm was implemented that uses sEMG data to identify the motion and tremor type of people living with PD when performing a task. Moreover, in order to automate the training process, hyperparamter selection was performed using a regularized evolutionary algorithm. The results show that the accuracy of task classification among 15 people living with PD was 84±8%, and the accuracy of tremor classification was 88±5%. Both models performed significantly above chance levels (20% and 33% for task and tremor classification, respectively). Thus, it was concluded that the trained models, based on using purely sEMG signals, could successfully identify the task and tremor types.

Development of an EMG-Based Muscle Health Model for Elbow Trauma Patients.

Wearable robotic braces have the potential to improve rehabilitative therapies for patients suffering from musculoskeletal (MSK) conditions. Ideally, a quantitative assessment of health would be incorporated into rehabilitative devices to monitor patient recovery. The purpose of this work is to develop a model to distinguish between the healthy and injured arms of elbow trauma patients based on electromyography (EMG) data. Surface EMG recordings were collected from the healthy and injured limbs of 30 elbow trauma patients while performing 10 upper-limb motions. Forty-two features and five feature sets were extracted from the data. Feature selection was performed to improve the class separation and to reduce the computational complexity of the feature sets. The following classifiers were tested: linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF). The classifiers were used to distinguish between two levels of health: healthy and injured (50% baseline accuracy rate). Maximum fractal length (MFL), myopulse percentage rate (MYOP), power spectrum ratio (PSR) and spike shape analysis features were identified as the best features for classifying elbow muscle health. A majority vote of the LDA classification models provided a cross-validation accuracy of 82.1%. The work described in this paper indicates that it is possible to discern between healthy and injured limbs of patients with MSK elbow injuries. Further assessment and optimization could improve the consistency and accuracy of the classification models. This work is the first of its kind to identify EMG metrics for muscle health assessment by wearable rehabilitative devices.

Evaluating Muscle Activation Models for Elbow Motion Estimation.

Adoption of wearable assistive technologies relies heavily on improvement of existing control system models. Knowing which models to use and how to improve them is difficult to determine due to the number of proposed solutions with relatively little broad comparisons. One type of these models, muscle activation models, describes the nonlinear relationship between neural inputs and mechanical activation of the muscle. Many muscle activation models can be found in the literature, but no comparison is available to guide the community on limitations and improvements. In this research, an EMG-driven elbow motion model is developed for the purpose of evaluating muscle activation models. Seven muscle activation models are used in an optimization procedure to determine which model has the best performance. Root mean square errors in muscle torque estimation range from 1.67-2.19 Nm on average over varying input trajectories. The computational resource demand was also measured during the optimization procedure, as it is an important aspect for determining if a model is feasible for use in a particular wearable assistive device. This study provides insight into the ability of these models to estimate elbow motion and the trade-off between estimation accuracy and computational demand.

Energy-Based Metrics for Arthroscopic Skills Assessment.

Minimally invasive skills assessment methods are essential in developing efficient surgical simulators and implementing consistent skills evaluation. Although numerous methods have been investigated in the literature, there is still a need to further improve the accuracy of surgical skills assessment. Energy expenditure can be an indication of motor skills proficiency. The goals of this study are to develop objective metrics based on energy expenditure, normalize these metrics, and investigate classifying trainees using these metrics. To this end, different forms of energy consisting of mechanical energy and work were considered and their values were divided by the related value of an ideal performance to develop normalized metrics. These metrics were used as inputs for various machine learning algorithms including support vector machines (SVM) and neural networks (NNs) for classification. The accuracy of the combination of the normalized energy-based metrics with these classifiers was evaluated through a leave-one-subject-out cross-validation. The proposed method was validated using 26 subjects at two experience levels (novices and experts) in three arthroscopic tasks. The results showed that there are statistically significant differences between novices and experts for almost all of the normalized energy-based metrics. The accuracy of classification using SVM and NN methods was between 70% and 95% for the various tasks. The results show that the normalized energy-based metrics and their combination with SVM and NN classifiers are capable of providing accurate classification of trainees. The assessment method proposed in this study can enhance surgical training by providing appropriate feedback to trainees about their level of expertise and can be used in the evaluation of proficiency.

Randomized control trial for evaluation of a hands-free pointer for surgical instruction during laparoscopic cholecystectomy.

INTRODUCTION: Training surgeons in minimally invasive surgery (MIS) requires surgical residents to operate under the direction of a consultant. The inability of the instructing surgeon to point at the laparoscopic monitor without releasing the instruments remains a barrier to effective instruction. The wireless hands-free surgical pointer (WHaSP) has been developed to aid instruction during MIS. METHODS: The objective of this study was to evaluate the effectiveness and likeability of the WHaSP as an instructional tool compared with the conventional methods. Data were successfully collected during 103 laparoscopic cholecystectomy procedures, which had been randomized to use or not use the WHaSP as a teaching tool. Audio and video from the surgeries were recorded and analyzed. Instructing surgeons, operating surgeons, and camera assistants provided feedback through a post-operative questionnaire that used a five-level Likert scale. The questionnaire results were analyzed using a Mann-Whitney U test. RESULTS: There were no negative effects on surgery completion time or instruction practice due to the use of the WHaSP. The number of times an instructor surgeon pointed to the laparoscopic screen with their hand was significantly reduced when the WHaSP was utilized (p < 0.001). The questionnaires showed that WHaSP users found it to be comfortable, easy to use, and easy to control. Compared to when the WHaSP was not used, users found that communication was more effective (p = 0.002), locations were easier to communicate (p < 0.001), and instructions were easier to follow (p = 0.005). CONCLUSIONS: The WHaSP system was successfully used in surgery. It integrated seamlessly into existing equipment within the operating room and did not affect flow. The positive outcomes of utilizing the WHaSP were improved communication in the OR, improved efficiency and safety of the surgery, easy to use, and comfortable to wear. The surgeons showed a preference for utilizing the WHaSP if given a choice.

Development of force-based metrics for skills assessment in minimally invasive surgery.

BACKGROUND: The loss of haptic information that results from the reduced-access conditions present in minimally invasive surgery (MIS) may compromise the safety of the procedures. This limitation must be overcome through training. However, current methods for determining the skill level of trainees do not measure critical elements of skill attainment. This study aimed to evaluate the usefulness of force information for the assessment of skill during MIS. METHODS: To achieve the study goal, experiments were performed using a set of sensorized instruments capable of measuring instrument position and tissue interaction forces. Several force-based metrics were developed as well as metrics that combine force and position information. RESULTS: The results show that experience level has a strong correlation with the new force-based metrics presented in this article. In particular, the integral and the derivative of the forces or the metrics that combine force and position provide the strongest correlations. CONCLUSIONS: This study showed that force-based metrics are better indications of performance than metrics based on task completion time or position information alone. The proposed metrics can be automatically computed, are completely objective, and measure important aspects of performance.

Leveraging Space-Flown Technologies to Deliver Healthcare with Holographic Physical Examinations.

INTRODUCTION: Musculoskeletal injuries are one of the more common injuries in spaceflight. Physical assessment of an injury is essential for diagnosis and treatment. Unfortunately, when musculoskeletal injuries occur in space, the flight surgeon is limited to two-dimensional videoconferencing and, potentially, observations made by the crew medical officer. To address these limitations, we investigated the feasibility of performing physical examinations on a three-dimensional augmented reality projection using a mixed-reality headset, specifically evaluating a standard shoulder examination.METHODS: A simulated patient interaction was set up between Western University in London, Ontario, Canada, and Huntsville, AL, United States. The exam was performed by a medical student, and a healthy adult man volunteered to enable the physical exam.RESULTS: All parts of the standard shoulder physical examination according to the Bates Guide to the Physical Exam were performed with holoportation. Adaptation was required for the palpation and some special tests.DISCUSSION: All parts of the physical exam were able to be completed. The true to anatomical size of the holograms permitted improved inspection of the anatomy compared to traditional videoconferencing. Palpation was completed by instructing the patient to palpate themselves and comment on relevant findings asked by the examiner. Range of motion and special tests for specific pathologies were also able to be completed with some modifications due to the examiner not being present to provide resistance. Future work should aim to improve the graphics, physician communication, and haptic feedback during holoportation.Levschuk A, Whittal J, Trejos AL, Sirek A. Leveraging space-flown technologies to deliver healthcare with holographic physical examinations. Aerosp Med Hum Perform. 2024; 95(4):214-218.

Force sensing-based simulator for arthroscopic skills assessment in orthopaedic knee surgery.

The complexity of knee arthroscopy makes it difficult to teach and assess skill level during real surgery. Simulator-based training is ideal for this complex procedure. To address the limitations of existing systems, a physical simulator, capable of providing skills assessment and feedback has been developed. The simulator measures the forces applied on the femur and acting on the tools. An experimental evaluation was conducted to assess the differences in task completion time and applied forces for fourteen tasks performed by trainees and expert surgeons. Initial results show that the simulator, together with well-chosen tasks, can potentially be used to assess user performance.

Active cooling of twisted coiled actuators via fabric air channels.

Twisted coiled actuators (TCAs) are promising artificial muscles for wearable soft robotic devices due to their biomimetic properties, inherent compliance, and slim profile. These artificial muscles are created by super-coiling nylon thread and are thermally actuated. Unfortunately, their slow natural cooling rate limits their feasibility when used in wearable devices for upper limb rehabilitation. Thus, a novel cooling apparatus for TCAs was specifically designed for implementation in soft robotic devices. The cooling apparatus consists of a flexible fabric channel made from nylon pack cloth. The fabric channel is lightweight and could be sewn onto other garments for assembly into a soft robotic device. The TCA is placed in the channel, and a miniature air pump is used to blow air through it to enable active cooling. The impact of channel size on TCA performance was assessed by testing nine fabric channel sizes-combinations of three widths (6, 8, and 10 mm) and three heights (4, 6, and 8 mm). Overall, the performance of the TCA improved as the channel dimensions increased, with the combination of a 10 mm width and an 8 mm height resulting in the best balance between cooling time, heating time, and stroke. This channel was utilized in a follow-up experiment to determine the impact of the cooling apparatus on TCA performance. In comparison to passive cooling without a channel, the channel and miniature air pump reduced the TCA cooling time by 42% ( 21.71 ± 1.24 s to 12.54 ± 2.31 s, p < 0.001 ). Unfortunately, there was also a 9% increase in the heating time ( 3.46 ± 0.71 s to 3.76 ± 0.71 s, p < 0.001 ) and a 28% decrease in the stroke ( 5.40 ± 0.44 mm to 3.89 ± 0.77 mm, p < 0.001 ). This work demonstrates that fabric cooling channels are a viable option for cooling thermally actuated artificial muscles within a soft wearable device. Future work can continue to improve the channel design by experimenting with other configurations and materials.

Preliminary Assessment of the Safety of a Fault-Tolerant Control-based Wearable Tremor Suppression Glove.

The advent of wearable tremor suppression de-vices (WTSDs) has provided a promising alternative approach for parkinsonian tremor management, especially for individuals whose tremors are not managed by conventional treatment options. Currently, research in WTSDs has shown successful results with a tremor suppression ratio of up to 99 %; however, the user safety of WTSDs has not been properly considered, especially in the occurrence of unexpected events, such as faults and disturbances. In this study, a fault-tolerant control system was developed and integrated into the control system of a WTSD for the first time. The safety and tremor suppression performance of the proposed system under the influence of a measurement loss fault were tested and evaluated on 18 tremor motion datasets, specifically by quantifying the tremor power suppression ratio and the error when tracking voluntary motion. The experimental evaluation showed that the proposed system could remain functional and safe to use in the existence of the fault, with an average user motion tracking error of 1.5º. It was also found that the proposed system achieved significantly improved performance in both metrics when compared to the system without a fault-tolerant controller. Clinical Relevance-This work improves the safety and robustness of WTSDs making them more suitable for use as an additional treatment for parkinsonian tremor.

Real-Time Performance Assessment of High-Order Tremor Estimators Used in a Wearable Tremor Suppression Device.

The side effects and complications of traditional treatments for treating pathological tremor have led to a growing research interest in wearable tremor suppression devices (WTSDs) as an alternative approach. Similar to how the human brain coordinates the function of the human system, a tremor estimator determines how a WTSD functions. Although many tremor estimation algorithms have been developed and validated, whether they can be implemented on a cost-effective embedded system has not been studied; furthermore, their effectiveness on tremor signals with multiple harmonics has not been investigated. Therefore, in this study, four tremor estimators were implemented, evaluated, and compared: Weighted-frequency Fourier Linear Combiner (WFLC), WFLC-based Kalman Filter (WFLC-KF), Band-limited Multiple FLC, and enhanced High-order WFLC-KF (eHWFLC-KF). This study aimed to evaluate the performance of each algorithm on a bench-top tremor suppression system with 18 recorded tremor motion datasets; and compare the performance of each estimator. The experimental evaluation showed that the eHWFLC-KF-based WTSD achieved the best performance when suppressing tremor with an average of 89.3% reduction in tremor power, and an average error when tracking voluntary motion of 6.6°/s. Statistical analysis indicated that the eHWFLC-KF-based WTSD is able to reduce the power of tremor better than the WFLC and WFLC-KF, and the BMFLC-based WTSD is better than the WFLC. The performance when tracking voluntary motion is similar among all systems. This study has proven the feasibility of implementing various tremor estimators in a cost-effective embedded system, and provided a real-time performance assessment of four tremor estimators.

Identifying Interaction Forces Via EMG Under Changing Motion Dynamics.

Musculoskeletal injuries can severely inhibit performance of activities of daily living. In order to regain function, rehabilitation is often required. Assistive rehabilitation devices can be used to increase arm mobility by guiding therapeutic exercises or assisting with motion. Electromyography (EMG) may be able to provide an intuitive interface between the patient and the device if appropriate classification models allow smart systems to relate these signals to the desired device motion. Unfortunately, the accuracy of pattern recognition models classifying motion in constrained laboratory environments significantly drops when used for detecting dynamic unconstrained movements. The objectives of this study were to quantity how various motion factors affect arm muscle activations during dynamic motion, and to use these motion factors and EMG signals for detecting interaction forces between the person and the environment during motion. The results quantity how EMG features change significantly with variations in arm positions, interaction forces, and motion velocities. The results also show that pattern recognition models were able to detect intended characteristics of motion based solely on EMG signals. Prediction of force was improved from 73.77% correct to 79.17% accuracy during elbow flexion-extension by properly selecting the features, and providing measurable arm position and velocity information as additional inputs to a linear discriminant analysis model.

Survey-based identification of design requirements and constraints for a wearable tremor suppression device.

Introduction: Parkinsonian tremor has severely impacted the lives of 65% of individuals with Parkinson's disease, and nearly 25% do not respond to traditional treatments. Although wearable tremor suppression devices (WTSDs) have become a promising alternative approach, this technology is still in the early stages of development, and no studies have reported the stakeholders' opinions on this technology and their desired design requirements. Methods: An online survey was distributed to affected Canadians and Canadian movement disorder specialists (MDS) to acquire information on demographics, the current state of treatments, opinions on the WTSDs, and the desired design requirements of future WTSDs. Results: A total of 101 affected individuals and 24 MDS completed the survey. It was found that both groups are generally open to using WTSDs to manage tremor. The most important design requirement to end users is the adaptability to lifestyle, followed by weight and size, accurate motion, comfort, safety, quick response, and cost. Lastly, most of the participants (65%) think that the device should cost under $500. Conclusions: The findings from this study can be used as guidelines for the development of future WTSDs, such that the future generations could be evaluated and accepted by the end users.

Comparison of Machine Learning Techniques for Activities of Daily Living Classification with Electromyographic Data.

Advances in data science and wearable robotic devices present an opportunity to improve rehabilitation outcomes. Some of these devices incorporate electromyography (EMG) electrodes that sense physiological patient activity, making it possible to develop rehabilitation systems able to assess the patient's progress when performing activities of daily living (ADLs). However, additional research is needed to improve the ability to interpret EMG signals. To address this issue, an off-line classification approach for the 26 upper-limb ADLs included in the KIN-MUS UJI dataset is presented in this paper. The ADLs were performed by 22 subjects, while seven EMG signals were recorded from their forearms. From variable-length EMG time windows, 18 features were computed, and 13 features more were extracted from frequency domain windows. The classification performance of five different machine learning techniques, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU) network, XGBoost, and Random Forests, were compared. CNN performed best amongst individual models, with an accuracy above 80%, compared to SVM with 77%, GRU with 73.9%, and the tree-based models below 64%. Ensemble learning with four CNN models achieved an even higher accuracy of 86%. These results suggest that the CNN ensemble model is capable of classifying EMG signals for most ADLs, which could be used in off-line quantitative assessment of robotic rehabilitation outcomes.

Force sensing in natural orifice transluminal endoscopic surgery.

BACKGROUND: Natural orifice transluminal endoscopic surgery (NOTES) may represent the next frontier for therapeutic minimally invasive surgery; however, its feasibility is currently limited by the lack of suitable instruments. Identifying the forces required to manipulate tissue during NOTES is a necessary first step in the development of better instrumentation. METHODS: Sensorized instruments were used to measure the forces acting at the tip of the instruments during transgastric and transperineal NOTES procedures performed in two female pigs. The maximum and average forces when handling tissue were determined and compared. RESULTS: The results show that, for the transgastric approach, the average forces required are significantly less than in the transperineal approach (43% less), and that the maximum forces required are almost 8 and 16 N in the transgastric and transperineal approaches, respectively. The forces were higher than 5 N in 1.6% of the measurements in the transgastric approach and 2.9% in the transperineal approach. CONCLUSIONS: This study presents an experimental measurement of tissue manipulation forces in a NOTES procedure. This information may be valuable for research groups interested in developing NOTES instruments and devices. It is recommended that NOTES instruments be designed to easily handle forces as high as 16 N.

Evaluating Convolutional Neural Networks as a Method of EEG-EMG Fusion.

Wearable robotic exoskeletons have emerged as an exciting new treatment tool for disorders affecting mobility; however, the human-machine interface, used by the patient for device control, requires further improvement before robotic assistance and rehabilitation can be widely adopted. One method, made possible through advancements in machine learning technology, is the use of bioelectrical signals, such as electroencephalography (EEG) and electromyography (EMG), to classify the user's actions and intentions. While classification using these signals has been demonstrated for many relevant control tasks, such as motion intention detection and gesture recognition, challenges in decoding the bioelectrical signals have caused researchers to seek methods for improving the accuracy of these models. One such method is the use of EEG-EMG fusion, creating a classification model that decodes information from both EEG and EMG signals simultaneously to increase the amount of available information. So far, EEG-EMG fusion has been implemented using traditional machine learning methods that rely on manual feature extraction; however, new machine learning methods have emerged that can automatically extract relevant information from a dataset, which may prove beneficial during EEG-EMG fusion. In this study, Convolutional Neural Network (CNN) models were developed using combined EEG-EMG inputs to determine if they have potential as a method of EEG-EMG fusion that automatically extracts relevant information from both signals simultaneously. EEG and EMG signals were recorded during elbow flexion-extension and used to develop CNN models based on time-frequency (spectrogram) and time (filtered signal) domain image inputs. The results show a mean accuracy of 80.51 ± 8.07% for a three-class output (33.33% chance level), with an F-score of 80.74%, using time-frequency domain-based models. This work demonstrates the viability of CNNs as a new method of EEG-EMG fusion and evaluates different signal representations to determine the best implementation of a combined EEG-EMG CNN. It leverages modern machine learning methods to advance EEG-EMG fusion, which will ultimately lead to improvements in the usability of wearable robotic exoskeletons.

Real-Time Voluntary Motion Prediction and Parkinson's Tremor Reduction Using Deep Neural Networks.

Wearable tremor suppression devices (WTSD) have been considered as a viable solution to manage parkinsonian tremor. WTSDs showed their ability to improve the quality of life of individuals suffering from parkinsonian tremor, by helping them to perform activities of daily living (ADL). Since parkinsonian tremor has been shown to be nonstationary, nonlinear, and stochastic in nature, the performance of the tremor models used by WTSDs is affected by their inability to adapt to the nonlinear behaviour of tremor. Another drawback that the models have is their limitation to estimate or predict one step ahead, which introduces delay when used in real time with WTSDs, which compromises performance. To address these issues, this work proposes a deep neural network model that learns the correlations and nonlinearities of tremor and voluntary motion, and is capable of multi-step prediction with minimal delay. A generalized model that is task and user-independent is presented. The model achieved an average estimation percentage accuracy of 99.2%. The average future voluntary motion prediction percentage accuracy with 10, 20, 50, and 100 steps ahead was 97.0%, 94.0%, 91.6%, and 89.9%, respectively, with prediction time as low as 1.5 ms for 100 steps ahead. The proposed model also achieved an average of 93.8% ± 1.5% in tremor reduction when it was tested in an experimental setup in real time. The tremor reduction showed an improvement of 25% over the Weighted Fourier Linear Combiner (WFLC), an estimator commonly used with WTSDs.