Role of arm reaching movement kinematics in friction perception at initial contact with smooth surfaces.

When manipulating objects, humans begin adjusting their grip force to friction within 100 ms of contact. During motor adaptation, subjects become aware of the slipperiness of touched surfaces. Previously, we have demonstrated that humans cannot perceive frictional differences when surfaces are brought in contact with an immobilised finger, but can do so when there is submillimeter lateral displacement or subjects actively make the contact movement. Similarly, in, we investigated how humans perceive friction in the absence of intentional exploratory sliding or rubbing movements, to mimic object manipulation interactions. We used a two-alternative forced-choice paradigm in which subjects had to reach and touch one surface followed by another, and then indicate which felt more slippery. Subjects correctly identified the more slippery surface in 87 ± 8% of cases (mean ± SD; n = 12). Biomechanical analysis of finger pad skin displacement patterns revealed the presence of tiny (<1 mm) localised slips, known to be sufficient to perceive frictional differences. We tested whether these skin movements arise as a result of natural hand reaching kinematics. The task was repeated with the introduction of a hand support, eliminating the hand reaching movement and minimising fingertip movement deviations from a straight path. As a result, our subjects' performance significantly declined (66 ± 12% correct, mean ± SD; n = 12), suggesting that unrestricted reaching movement kinematics and factors such as physiological tremor, play a crucial role in enhancing or enabling friction perception upon initial contact. KEY POINTS: More slippery objects require a stronger grip to prevent them from slipping out of hands. Grip force adjustments to friction driven by tactile sensory signals are largely automatic and do not necessitate cognitive involvement; nevertheless, some associated awareness of grip surface slipperiness under such sensory conditions is present and helps to select a safe and appropriate movement plan. When gripping an object, tactile receptors provide frictional information without intentional rubbing or sliding fingers over the surface. However, we have discovered that submillimeter range lateral displacement might be required to enhance or enable friction sensing. The present study provides evidence that such small lateral movements causing localised partial slips arise and are an inherent part of natural reaching movement kinematics.

How Tactile Afferents in the Human Fingerpad Encode Tangential Torques Associated with Manipulation: Are Monkeys Better than Us?

Dexterous object manipulation depends critically on information about forces normal and tangential to the fingerpads, and also on torque associated with object orientation at grip surfaces. We investigated how torque information is encoded by human tactile afferents in the fingerpads and compared them to 97 afferents recorded in monkeys (n = 3; 2 females) in our previous study. Human data included slowly-adapting Type-II (SA-II) afferents, which are absent in the glabrous skin of monkeys. Torques of different magnitudes (3.5-7.5 mNm) were applied in clockwise and anticlockwise directions to a standard central site on the fingerpads of 34 human subjects (19 females). Torques were superimposed on a 2, 3, or 4 N background normal force. Unitary recordings were made from fast-adapting Type-I (FA-I, n = 39), and slowly-adapting Type-I (SA-I, n = 31) and Type-II (SA-II, n = 13) afferents supplying the fingerpads via microelectrodes inserted into the median nerve. All three afferent types encoded torque magnitude and direction, with torque sensitivity being higher with smaller normal forces. SA-I afferent responses to static torque were inferior to dynamic stimuli in humans, while in monkeys the opposite was true. In humans this might be compensated by the addition of sustained SA-II afferent input, and their capacity to increase or decrease firing rates with direction of rotation. We conclude that the discrimination capacity of individual afferents of each type was inferior in humans than monkeys which could be because of differences in fingertip tissue compliance and skin friction.SIGNIFICANCE STATEMENT We investigated how individual human tactile nerve fibers encode rotational forces (torques) and compared them to their monkey counterparts. Human hands, but not monkey hands, are innervated by a tactile neuron type (SA-II afferents) specialized to encode directional skin strain yet, so far, torque encoding has only been studied in monkeys. We find that human SA-I afferents were generally less sensitive and less able to discriminate torque magnitude and direction than their monkey counterparts, especially during the static phase of torque loading. However, this shortfall in humans could be compensated by SA-II afferent input. This indicates that variation in afferent types might complement each other signaling different stimulus features possibly providing computational advantage to discriminate stimuli.

Submillimeter Lateral Displacement Enables Friction Sensing and Awareness of Surface Slipperiness.

Human tactile perception and motor control rely on the frictional estimates that stem from the deformation of the skin and slip events. However, it is not clear how exactly these mechanical events relate to the perception of friction. This study aims to quantify how minor lateral displacement and speed enables subjects to feel frictional differences. In a 2-alternative forced-choice protocol, an ultrasonic friction-reduction device was brought in contact perpendicular to the skin surface of an immobilized index finger; after reaching 1N normal force, the plate was moved laterally. A combination of four displacement magnitudes (0.2, 0.5, 1.2 and 2 mm), two levels of friction (high, low) and three displacement speeds (1, 5 and 10 mm/s) were tested. We found that the perception of frictional difference was enabled by submillimeter range lateral displacement. Friction discrimination thresholds were reached with lateral displacements ranging from 0.2 to 0.5 mm and surprisingly speed had only a marginal effect. These results demonstrate that partial slips are sufficient to cause awareness of surface slipperiness. These quantitative data are crucial for designing haptic devices that render slipperiness. The results also show the importance of subtle lateral finger movements present during dexterous manipulation tasks.

Friction sensing mechanisms for perception and motor control: passive touch without sliding may not provide perceivable frictional information.

Perception of the frictional properties of a surface contributes to the multidimensional experience of exploring various materials; we slide our fingers over a surface to feel it. In contrast, during object manipulation, we grip objects without such intended exploratory movements. Given that we are aware of the slipperiness of objects or tools that are held in the hand, we investigated whether the initial contact between the fingertip skin and the surface of the object is sufficient to provide this consciously perceived frictional information. Using a two-alternative forced-choice protocol, we examined human capacity to detect frictional differences using touch, when two otherwise structurally identical surfaces were brought in contact with the immobilized finger perpendicularly or under an angle (20° or 30°) to the skin surface (passive touch). An ultrasonic friction reduction device was used to generate three different frictions over each of three flat surfaces with different surface structure: 1) smooth glass, 2) textured surface with dome-shaped features, and 3) surface with sharp asperities (sandpaper). Participants (n = 12) could not reliably indicate which of the two surfaces was more slippery under any of these conditions. In contrast, when slip was induced by moving the surface laterally by a total of 5 mm (passive slip), participants could clearly perceive frictional differences. Thus making contact with the surface, even with moderate tangential forces, was not enough to perceive frictional differences, instead conscious perception required a sufficient size slip.NEW & NOTEWORTHY This study contributes to understanding how frictional information is obtained and used by the brain. When the skin is contacting surfaces of identical topography but varying frictional properties, the deformation pattern is different; however, available sensory cues did not get translated into perception of frictional properties unless a sufficiently large lateral movement was present. These neurophysiological findings may inform how to design and operate haptic devices relying on friction modulation principles.

Estimating Lower Limb Kinematics Using a Reduced Wearable Sensor Count.

GOAL: This paper presents an algorithm for accurately estimating pelvis, thigh, and shank kinematics during walking using only three wearable inertial sensors. METHODS: The algorithm makes novel use of a constrained Kalman filter (CKF). The algorithm iterates through the prediction (kinematic equation), measurement (pelvis position pseudo-measurements, zero velocity update, flat-floor assumption, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints). RESULTS: Evaluation of the algorithm using an optical motion capture-based sensor-to-segment calibration on nine participants (7 men and 2 women, weight [Formula: see text] kg, height [Formula: see text] m, age [Formula: see text] years old), with no known gait or lower body biomechanical abnormalities, who walked within a [Formula: see text] m 2 capture area shows that it can track motion relative to the mid-pelvis origin with mean position and orientation (no bias) root-mean-square error (RMSE) of [Formula: see text] cm and [Formula: see text], respectively. The sagittal knee and hip joint angle RMSEs (no bias) were [Formula: see text] and [Formula: see text], respectively, while the corresponding correlation coefficient (CC) values were [Formula: see text] and [Formula: see text]. CONCLUSION: The CKF-based algorithm was able to track the 3D pose of the pelvis, thigh, and shanks using only three inertial sensors worn on the pelvis and shanks. SIGNIFICANCE: Due to the Kalman-filter-based algorithm's low computation cost and the relative convenience of using only three wearable sensors, gait parameters can be computed in real-time and remotely for long-term gait monitoring. Furthermore, the system can be used to inform real-time gait assistive devices.

A Biomimetic Approach toward Enhancing Angiogenesis: Recombinantly Expressed Domain V of Human Perlecan Is a Bioactive Molecule That Promotes Angiogenesis and Vascularization of Implanted Biomaterials.

Angiogenic therapy involving delivery of pro-angiogenic growth factors to stimulate new blood vessel formation in ischemic disease is promising but has seen limited clinical success due to issues associated with the need to deliver supra-physiological growth factor concentrations. Bio-inspired growth factor delivery utilizing the native growth factor signaling roles of the extracellular matrix proteoglycans has the potential to overcome many of the drawbacks of angiogenic therapy. In this study, the potential of the recombinantly expressed domain V (rDV) of human perlecan is investigated as a means of promoting growth factor signaling toward enhanced angiogenesis and vascularization of implanted biomaterials. rDV is found to promote angiogenesis in established in vitro and in vivo angiogenesis assays by potentiating endogenous growth factor signaling via its glycosaminoglycan chains. Further, rDV is found to potentiate fibroblast growth factor 2 (FGF2) signaling at low concentrations that in the absence of rDV are not biologically active. Finally, rDV immobilized on 3D porous silk fibroin biomaterials promotes enhanced vascular ingrowth and integration of the implanted scaffolds with the surrounding tissue. Together, these studies demonstrate the important role of this biologically active perlecan fragment and its potential in the treatment of ischemia in both native and bioengineered tissues.

Detection of Atrial Fibrillation from RR Intervals and PQRST Morphology using a Neural Network Ensemble.

Early detection and discrimination of cardiac arrhythmia, atrial fibrillation (AF) in particular, is essential for timely intervention to improve patient outcomes. In this work, an algorithm was developed to classify ECG records as normal, AF, other arrhythmia, or too noisy to classify. This algorithm, which was an entry for the PhysioNet Computing in Cardiology Challenge 2017 (the Challenge), is described. Artifact masking and QRS detection were applied to lead-I equivalent ECG records and 17 features were extracted which captured the irregularity of the RR intervals, the PQRST morphology, and artifact/noise. An ensemble of ten neural networks (NN) was trained on the features from a training set of 5,970 records. A final classification was taken by majority vote over the 10 classifiers. The trained NN models were validated on a further 2,558 ECG records and then tested on a blind out-of-sample test set of 3,658 records. A mean $F_{1}$ score across the four classes of 0.78 for the training/validation sets and 0.80 for the testing set was achieved. A higher $F_{1}$ score for the testing set indicates that overtraining did not occur, unlike most entries to the Challenge (winner mean $F_{1}$ score of 0.89 for training/validation set, and 0.83 for testing set). Performance of the Challenge winner was not ideal and there is evidence of overtraining, indicating the difficulty of classifying AF from single-lead ECG. The features and method described here performed comparably and overtraining did not occur (high likelihood of generalization) indicating a good starting point for future work.

Single camera multi-view anthropometric measurement of human height and mid-upper arm circumference using linear regression.

BACKGROUND: Manually measured anthropometric quantities are used in many applications including human malnutrition assessment. Training is required to collect anthropometric measurements manually, which is not ideal in resource-constrained environments. Photogrammetric methods have been gaining attention in recent years, due to the availability and affordability of digital cameras. OBJECTIVE: The primary goal is to demonstrate that height and mid-upper arm circumference (MUAC)-indicators of malnutrition-can be accurately estimated by applying linear regression to distance measurements from photographs of participants taken from five views, and determine the optimal view combinations. A secondary goal is to observe the effect on estimate error of two approaches which reduce complexity of the setup, computational requirements and the expertise required of the observer. METHODS: Thirty-one participants (11 female, 20 male; 18-37 years) were photographed from five views. Distances were computed using both camera calibration and reference object techniques from manually annotated photos. To estimate height, linear regression was applied to the distances between the top of the participants head and the floor, as well as the height of a bounding box enclosing the participant's silhouette which eliminates the need to identify the floor. To estimate MUAC, linear regression was applied to the mid-upper arm width. Estimates were computed for all view combinations and performance was compared to other photogrammetric methods from the literature-linear distance method for height, and shape models for MUAC. RESULTS: The mean absolute difference (MAD) between the linear regression estimates and manual measurements were smaller compared to other methods. For the optimal view combinations (smallest MAD), the technical error of measurement and coefficient of reliability also indicate the linear regression methods are more reliable. The optimal view combination was the front and side views. When estimating height by linear regression of the distance from the head to the floor, the mean MAD was 10.51 mm ± 6.52 mm SD, and when estimating height from the bounding box using the reference object, the mean MAD per participant was 11.53 mm ± 6.43 mm SD. When estimating MUAC from the mid-upper arm radius using the reference object, the mean MAD was 7.24 mm ± 4.79 mm SD. The mean MAD for all methods when using camera calibration was 2-3 mm smaller. CONCLUSIONS: Applying linear regression to distance measurements from photos of adults taken from multiple view angles has been shown to accurately estimate height and MUAC to within the accuracy required for nutrition assessment. Future work will focus on automating the landmark detection, and validating the methods on populations that include undernourished adults and children of all nutrition statuses. These future works will improve the practicality of this method as a potential tool for nutrition assessment by novice users.

QRS Detection Algorithm for Telehealth Electrocardiogram Recordings.

OBJECTIVE: QRS detection algorithms are needed to analyze electrocardiogram (ECG) recordings generated in telehealth environments. However, the numerous published QRS detectors focus on clean clinical data. Here, a "UNSW" QRS detection algorithm is described that is suitable for clinical ECG and also poorer quality telehealth ECG. METHODS: The UNSW algorithm generates a feature signal containing information about ECG amplitude and derivative, which is filtered according to its frequency content and an adaptive threshold is applied. The algorithm was tested on clinical and telehealth ECG and the QRS detection performance is compared to the Pan-Tompkins (PT) and Gutiérrez-Rivas (GR) algorithm. RESULTS: For the MIT-BIH Arrhythmia database (virtually artifact free, clinical ECG), the overall sensitivity (Se) and positive predictivity (+P) of the UNSW algorithm was >99%, which was comparable to PT and GR. When applied to the MIT-BIH noise stress test database (clinical ECG with added calibrated noise) after artifact masking, all three algorithms had overall Se >99%, and the UNSW algorithm had higher +P (98%, p < 0.05) than PT and GR. For 250 telehealth ECG records (unsupervised recordings; dry metal electrodes), the UNSW algorithm had 98% Se and 95% +P which was superior to PT (+P: p < 0.001) and GR (Se and +P: p < 0.001). CONCLUSION: This is the first study to describe a QRS detection algorithm for telehealth data and evaluate it on clinical and telehealth ECG with superior results to published algorithms. SIGNIFICANCE: The UNSW algorithm could be used to manage increasing telehealth ECG analysis workloads.

The Bayesian Decoding of Force Stimuli from Slowly Adapting Type I Fibers in Humans.

It is well known that signals encoded by mechanoreceptors facilitate precise object manipulation in humans. It is therefore of interest to study signals encoded by the mechanoreceptors because this will contribute further towards the understanding of fundamental sensory mechanisms that are responsible for coordinating force components during object manipulation. From a practical point of view, this may suggest strategies for designing sensory-controlled biomedical devices and robotic manipulators. We use a two-stage nonlinear decoding paradigm to reconstruct the force stimulus given signals from slowly adapting type one (SA-I) tactile afferents. First, we describe a nonhomogeneous Poisson encoding model which is a function of the force stimulus and the force's rate of change. In the decoding phase, we use a recursive nonlinear Bayesian filter to reconstruct the force profile, given the SA-I spike patterns and parameters described by the encoding model. Under the current encoding model, the mode ratio of force to its derivative is: 1.26 to 1.02. This indicates that the force derivative contributes significantly to the rate of change to the SA-I afferent spike modulation. Furthermore, using recursive Bayesian decoding algorithms is advantageous because it can incorporate past and current information in order to make predictions--consistent with neural systems--with little computational resources. This makes it suitable for interfacing with prostheses.

Decoding tactile afferent activity to obtain an estimate of instantaneous force and torque applied to the fingerpad.

Dexterous manipulation is not possible without sensory information about object properties and manipulative forces. Fundamental neuroscience has been unable to demonstrate how information about multiple stimulus parameters may be continuously extracted, concurrently, from a population of tactile afferents. This is the first study to demonstrate this, using spike trains recorded from tactile afferents innervating the monkey fingerpad. A multiple-regression model, requiring no a priori knowledge of stimulus-onset times or stimulus combination, was developed to obtain continuous estimates of instantaneous force and torque. The stimuli consisted of a normal-force ramp (to a plateau of 1.8, 2.2, or 2.5 N), on top of which -3.5, -2.0, 0, +2.0, or +3.5 mNm torque was applied about the normal to the skin surface. The model inputs were sliding windows of binned spike counts recorded from each afferent. Models were trained and tested by 15-fold cross-validation to estimate instantaneous normal force and torque over the entire stimulation period. With the use of the spike trains from 58 slow-adapting type I and 25 fast-adapting type I afferents, the instantaneous normal force and torque could be estimated with small error. This study demonstrated that instantaneous force and torque parameters could be reliably extracted from a small number of tactile afferent responses in a real-time fashion with stimulus combinations that the model had not been exposed to during training. Analysis of the model weights may reveal how interactions between stimulus parameters could be disentangled for complex population responses and could be used to test neurophysiologically relevant hypotheses about encoding mechanisms.

An eight-legged tactile sensor to estimate coefficient of static friction.

It is well known that a tangential force larger than the maximum static friction force is required to initiate the sliding motion between two objects, which is governed by a material constant called the coefficient of static friction. Therefore, knowing the coefficient of static friction is of great importance for robot grippers which wish to maintain a stable and precise grip on an object during various manipulation tasks. Importantly, it is most useful if grippers can estimate the coefficient of static friction without having to explicitly explore the object first, such as lifting the object and reducing the grip force until it slips. A novel eight-legged sensor, based on simplified theoretical principles of friction is presented here to estimate the coefficient of static friction between a planar surface and the prototype sensor. Each of the sensor's eight legs are straight and rigid, and oriented at a specified angle with respect to the vertical, allowing it to estimate one of five ranges (5 = 8/2 + 1) that the coefficient of static friction can occupy. The coefficient of friction can be estimated by determining whether the legs have slipped or not when pressed against a surface. The coefficients of static friction between the sensor and five different materials were estimated and compared to a measurement from traditional methods. A least-squares linear fit of the sensor estimated coefficient showed good correlation with the reference coefficient with a gradient close to one and an r(2) value greater than 0.9.

Tactile afferents encode grip safety before slip for different frictions.

Adjustments to frictional forces are crucial to maintain a safe grip during precision object handling in both humans and robotic manipulators. The aim of this work was to investigate whether a population of human tactile afferents can provide information about the current tangential/normal force ratio expressed as the percentage of the critical load capacity - the tangential/normal force ratio at which the object would slip. A smooth stimulation surface was tested on the fingertip under three frictional conditions, with a 4 N normal force and a tangential force generated by motion in the ulnar or distal direction at a fixed speed. During stimulation, the responses of 29 afferents (12 SA-I, 2 SA-II, 12 FA-I, 3 FA-II) were recorded. A multiple regression model was trained and tested using cross-validation to estimate the percentage of the critical load capacity in real-time as the tangential force increased. The features for the model were the number of spikes from each afferent in windows of fixed length (50, 100 or 200 ms) around points spanning the range from 50% to 100% of the critical load capacity, in 5% increments. The mean regression estimate error was less than 1% of the critical load capacity with a standard deviation between 5% and 10%. A larger number of afferents is expected to improve the estimate error. This work is important for understanding human dexterous manipulation and inspiring improvements in robotic grippers and prostheses.

Frequency-moment signatures: a method for automated seizure detection from scalp EEG.

OBJECTIVES: To investigate patient-specific automated epileptic seizure detection from scalp EEG using a new technique: frequency-moment signatures. METHODS: Signatures were calculated from 32s blocks of data of electrode differences from the right (RH) and left hemisphere (LH). Discrete Fourier transforms of 15 data subsets were calculated per block per hemisphere. The spectral powers at a given frequency from the RH and LH were combined into a single quantity. The signature elements were found by subtracting normalised central moments of the subset distribution from the mean, to measure the consistency of the spectral power at a given frequency over all subsets. The seizure measure was the logarithm of the probability that a signature belonged to a control set of non-seizure signatures. RESULTS: Following the optimisation of signature parameters using three one-day recordings from each of 12 patients, performance was tested on a separate set of data from the same patients. The method had a sensitivity of 91.0% (total 34 seizures) with 0.020 false positives per hour (total 618 h). CONCLUSIONS: Frequency-moment signatures promise automated seizure detection sensitivities comparable to visual identification and other published methods, with improved false detection rates. SIGNIFICANCE: This technique has the potential to be used more widely in EEG analysis.

Decoding tactile sensation: multiple regression analysis of monkey fingertip afferent mechanoreceptor population responses.

How complex tactile sensations are encoded by populations of afferent mechanoreceptors is currently not well understood. While much is known about how individual afferents respond to prescribed stimuli, their behavior as a population distributed across the fingertip has not been well described. In this study, tactile afferent mechanoreceptors in monkey fingertips were mechanically stimulated, using a flat disc shaped probe, with several magnitudes of normal force (1.8, 2.2 and 2.5 N) and torque (2.0 and 3.5 mNm), in clockwise and anticlockwise directions. Afferent nerve responses were acquired from 58 slowly-adapting (SA) type-I and 25 fast-adapting (FA) type-I isolated single cutaneous mechanoreceptive afferents, recorded from the median nerve. At 10 ms time intervals after the application of torque begins, a multiple regression model was trained and evaluated to estimate the magnitude of the applied normal force and torque. Averaged results over the 200 ms period after the torque reaches its maximum indicate that SA-I and FA-I afferents can both estimate the applied torque value. FA-I afferents gave the lowest estimation error mean and standard deviation of -0.051 ± 0.334 mNm for a target torque of 2.0 mNm, and 0.003 ± 0.414 mNm for a target torque of 3.5 mNm. However, while SA-I afferents could estimate normal force well, there was no significant difference (ANOVA, p=0.173) in the FA-I estimates of normal force, as this force had already been held constant for one second before the torque loading phase under analysis began.