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.

Design, Fabrication, and Characterization of a Novel Optical Six-Axis Distributed Force and Displacement Tactile Sensor for Dexterous Robotic Manipulation.

Real-time multi-axis distributed tactile sensing is a critical capability if robots are to perform stable gripping and dexterous manipulation, as it provides crucial information about the sensor-object interface. In this paper, we present an optical-based six-axis tactile sensor designed in a fingertip shape for robotic dexterous manipulation. The distributed sensor can precisely estimate the local XYZ force and displacement at ten distinct locations and provide the global XYZ force and torque measurements. Its compact size, comparable to that of a human thumb, and minimal thickness allow seamless integration onto existing robotic fingers, eliminating the need for complex modifications to the gripper. The proposed sensor design uses a simple, low-cost fabrication method. Moreover, the optical transduction approach uses light angle and intensity sensing to infer force and displacement from deformations of the individual sensing units that form the overall sensor, providing distributed six-axis sensing. The local force precision at each sensing unit in the X, Y, and Z axes is 20.89 mN, 19.19 mN, and 43.22 mN, respectively, over a local force range of approximately ±1.5 N in X and Y and 0 to -2 N in Z. The local displacement precision in the X, Y, and Z axes is 56.70 µm, 50.18 µm, and 13.83 µm, respectively, over a local displacement range of ±2 mm in the XY directions and 0 to -1.5 mm in Z (i.e., compression). Additionally, the sensor can measure global torques, Tx, Ty, and Tz, with a precision of of 1.90 N-mm, 1.54 N-mm, and 1.26 N-mm, respectively. The fabricated design is showcased by integrating it with an OnRobot RG2 gripper and illustrating real-time measurements during in simple demonstration task, which generated changing global forces and torques.

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 Lie Group Constrained Extended Kalman Filter with a Reduced Wearable IMU Count and Distance Measurements.

Tracking the kinematics of human movement usually requires the use of equipment that constrains the user within a room (e.g., optical motion capture systems), or requires the use of a conspicuous body-worn measurement system (e.g., inertial measurement units (IMUs) attached to each body segment). This paper presents a novel Lie group constrained extended Kalman filter to estimate lower limb kinematics using IMU and inter-IMU distance measurements in a reduced sensor count configuration. The algorithm iterates through the prediction (kinematic equations), measurement (pelvis height assumption/inter-IMU distance measurements, zero velocity update for feet/ankles, flat-floor assumption for feet/ankles, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints). The knee and hip joint angle root-mean-square errors in the sagittal plane for straight walking were 7.6±2.6∘ and 6.6±2.7∘, respectively, while the correlation coefficients were 0.95±0.03 and 0.87±0.16, respectively. Furthermore, experiments using simulated inter-IMU distance measurements show that performance improved substantially for dynamic movements, even at large noise levels (σ=0.2 m). However, further validation is recommended with actual distance measurement sensors, such as ultra-wideband ranging sensors.

Deep Learning for Activity Recognition in Older People Using a Pocket-Worn Smartphone.

Activity recognition can provide useful information about an older individual's activity level and encourage older people to become more active to live longer in good health. This study aimed to develop an activity recognition algorithm for smartphone accelerometry data of older people. Deep learning algorithms, including convolutional neural network (CNN) and long short-term memory (LSTM), were evaluated in this study. Smartphone accelerometry data of free-living activities, performed by 53 older people (83.8 ± 3.8 years; 38 male) under standardized circumstances, were classified into lying, sitting, standing, transition, walking, walking upstairs, and walking downstairs. A 1D CNN, a multichannel CNN, a CNN-LSTM, and a multichannel CNN-LSTM model were tested. The models were compared on accuracy and computational efficiency. Results show that the multichannel CNN-LSTM model achieved the best classification results, with an 81.1% accuracy and an acceptable model and time complexity. Specifically, the accuracy was 67.0% for lying, 70.7% for sitting, 88.4% for standing, 78.2% for transitions, 88.7% for walking, 65.7% for walking downstairs, and 68.7% for walking upstairs. The findings indicated that the multichannel CNN-LSTM model was feasible for smartphone-based activity recognition in older people.

Learning the Orientation of a Loosely-Fixed Wearable IMU Relative to the Body Improves the Recognition Rate of Human Postures and Activities.

Features were developed which accounted for the changing orientation of the inertial measurement unit (IMU) relative to the body, and demonstrably improved the performance of models for human activity recognition (HAR). The method is proficient at separating periods of standing and sedentary activity (i.e., sitting and/or lying) using only one IMU, even if it is arbitrarily oriented or subsequently re-oriented relative to the body; since the body is upright during walking, learning the IMU orientation during walking provides a reference orientation against which sitting and/or lying can be inferred. Thus, the two activities can be identified (irrespective of the cohort) by analyzing the magnitude of the angle of shortest rotation which would be required to bring the upright direction into coincidence with the average orientation from the most recent 2.5 s of IMU data. Models for HAR were trained using data obtained from a cohort of 37 older adults (83.9 ± 3.4 years) or 20 younger adults (21.9 ± 1.7 years). Test data were generated from the training data by virtually re-orienting the IMU so that it is representative of carrying the phone in five different orientations (relative to the thigh). The overall performance of the model for HAR was consistent whether the model was trained with the data from the younger cohort, and tested with the data from the older cohort after it had been virtually re-oriented (Cohen's Kappa 95% confidence interval [0.782, 0.793]; total class sensitivity 95% confidence interval [84.9%, 85.6%]), or the reciprocal scenario in which the model was trained with the data from the older cohort, and tested with the data from the younger cohort after it had been virtually re-oriented (Cohen's Kappa 95% confidence interval [0.765, 0.784]; total class sensitivity 95% confidence interval [82.3%, 83.7%]).

Automation of the Fetal Right Myocardial Performance Index to Optimise Repeatability.

OBJECTIVES: To compare the repeatability and degree of absolute agreement of an automated fetal right myocardial performance index (MPI) algorithm with manual measurements along with the impact of MPI observer experience on these two aspects. METHODS: Prospective cross-sectional study of 65 uncomplicated singleton pregnancies from 22 to 39 weeks' gestation. Image analysis of double-waveform right MPI measurements was conducted first with a MATLAB automated MPI software and then independently by an experienced and an inexperienced observer. Intraclass correlation coefficients (ICCs) and 95% confidence intervals (95% CI) were used to evaluate manual and automated intra- and interobserver repeatability. In addition, Bland-Altman plots were used to determine the degree of absolute agreement. RESULTS: Successful automation was performed on 63 cases (97%) showing repeatability ICCs of: 0.83 manual intraobserver; 0.77 manual interobserver; 1.00 automated. The degree of absolute agreement between manual and automated values was: inexperienced observer ICC 0.43 (95% CI 0.21-0.62); experienced observer ICC 0.76 (95% CI 0.63-0.85). CONCLUSION: Automation of right MPI demonstrates a superior reproducibility over manual measurements and reduces the experience required for successful analysis. This may lend a greater clinical applicability to MPI, and future studies to develop an automated universal reference range would be useful.

Characterisation and functional mapping of surface potentials in the rat dorsal column nuclei.

KEY POINTS: The brainstem dorsal column nuclei (DCN) process sensory information arising from the body before it reaches the brain and becomes conscious. Despite significant investigations into sensory coding in peripheral nerves and the somatosensory cortex, little is known about how sensory information arising from the periphery is represented in the DCN. Following stimulation of hind-limb nerves, we mapped and characterised the evoked electrical signatures across the DCN surface. We show that evoked responses recorded from the DCN surface are highly reproducible and are unique to nerves carrying specific sensory information. ABSTRACT: The brainstem dorsal column nuclei (DCN) play a role in early processing of somatosensory information arising from a variety of functionally distinct peripheral structures, before being transmitted to the cortex via the thalamus. To improve our understanding of how sensory information is represented by the DCN, we characterised and mapped low- (<200 Hz) and high-frequency (550-3300 Hz) components of nerve-evoked DCN surface potentials. DCN surface potentials were evoked by electrical stimulation of the left and right nerves innervating cutaneous structures (sural nerve), or a mix of cutaneous and deep structures (peroneal nerve), in 8-week-old urethane-anaesthetised male Wistar rats. Peroneal nerve-evoked DCN responses demonstrated low-frequency events with significantly longer durations, more high-frequency events and larger magnitudes compared to responses evoked from sural nerve stimulation. Hotspots of low- and high-frequency DCN activity were found ipsilateral to stimulated nerves but were not symmetrically organised. In conclusion, we find that sensory inputs from peripheral nerves evoke unique and characteristic DCN activity patterns that are highly reproducible both within and across animals.

Analyzing health insurance claims on different timescales to predict days in hospital.

Health insurers maintain large databases containing information on medical services utilized by claimants, often spanning several healthcare services and providers. Proper use of these databases could facilitate better clinical and administrative decisions. In these data sets, there exists many unequally spaced events, such as hospital visits. However, data mining of temporal data and point processes is still a developing research area and extracting useful information from such data series is a challenging task. In this paper, we developed a time series data mining approach to predict the number of days in hospital in the coming year for individuals from a general insured population based on their insurance claim data. In the proposed method, the data were windowed at four different timescales (bi-monthly, quarterly, half-yearly and yearly) to construct regularly spaced time series features extracted from such events, resulting in four associated prediction models. A comparison of these models indicates models using a half-yearly windowing scheme delivers the best performance on all three populations (the whole population, a senior sub-population and a non-senior sub-population). The superiority of the half-yearly model was found to be particularly pronounced in the senior sub-population. A bagged decision tree approach was able to predict 'no hospitalization' versus 'at least one day in hospital' with a Matthews correlation coefficient (MCC) of 0.426. This was significantly better than the corresponding yearly model, which achieved 0.375 for this group of customers. Further reducing the length of the analysis windows to three or two months did not produce further improvements.

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.

Tracking the Evolution of Smartphone Sensing for Monitoring Human Movement.

Advances in mobile technology have led to the emergence of the "smartphone", a new class of device with more advanced connectivity features that have quickly made it a constant presence in our lives. Smartphones are equipped with comparatively advanced computing capabilities, a global positioning system (GPS) receivers, and sensing capabilities (i.e., an inertial measurement unit (IMU) and more recently magnetometer and barometer) which can be found in wearable ambulatory monitors (WAMs). As a result, algorithms initially developed for WAMs that "count" steps (i.e., pedometers); gauge physical activity levels; indirectly estimate energy expenditure and monitor human movement can be utilised on the smartphone. These algorithms may enable clinicians to "close the loop" by prescribing timely interventions to improve or maintain wellbeing in populations who are at risk of falling or suffer from a chronic disease whose progression is linked to a reduction in movement and mobility. The ubiquitous nature of smartphone technology makes it the ideal platform from which human movement can be remotely monitored without the expense of purchasing, and inconvenience of using, a dedicated WAM. In this paper, an overview of the sensors that can be found in the smartphone are presented, followed by a summary of the developments in this field with an emphasis on the evolution of algorithms used to classify human movement. The limitations identified in the literature will be discussed, as well as suggestions about future research directions.

Improved Measurement of Blood Pressure by Extraction of Characteristic Features from the Cuff Oscillometric Waveform.

We present a novel approach to improve the estimation of systolic (SBP) and diastolic blood pressure (DBP) from oscillometric waveform data using variable characteristic ratios between SBP and DBP with mean arterial pressure (MAP). This was verified in 25 healthy subjects, aged 28 ± 5 years. The multiple linear regression (MLR) and support vector regression (SVR) models were used to examine the relationship between the SBP and the DBP ratio with ten features extracted from the oscillometric waveform envelope (OWE). An automatic algorithm based on relative changes in the cuff pressure and neighbouring oscillometric pulses was proposed to remove outlier points caused by movement artifacts. Substantial reduction in the mean and standard deviation of the blood pressure estimation errors were obtained upon artifact removal. Using the sequential forward floating selection (SFFS) approach, we were able to achieve a significant reduction in the mean and standard deviation of differences between the estimated SBP values and the reference scoring (MLR: mean ± SD = -0.3 ± 5.8 mmHg; SVR and -0.6 ± 5.4 mmHg) with only two features, i.e., Ratio2 and Area3, as compared to the conventional maximum amplitude algorithm (MAA) method (mean ± SD = -1.6 ± 8.6 mmHg). Comparing the performance of both MLR and SVR models, our results showed that the MLR model was able to achieve comparable performance to that of the SVR model despite its simplicity.

Energy-Efficient Sleep Apnea Detection Using a Hyperdimensional Computing Framework Based on Wearable Bracelet Photoplethysmography.

OBJECTIVE: Sleep apnea syndrome (SAS) is a common sleep disorder, which has been shown to be an important contributor to major neurocognitive and cardiovascular sequelae. Considering current diagnostic strategies are limited with bulky medical devices and high examination expenses, a large number of cases go undiagnosed. To enable large-scale screening for SAS, wearable photoplethysmography (PPG) technologies have been used as an early detection tool. However, existing algorithms are energy-intensive and require large amounts of memory resources, which are believed to be the major drawbacks for further promotion of wearable devices for SAS detection. METHODS: In this paper, an energy-efficient method of SAS detection based on hyperdimensional computing (HDC) is proposed. Inspired by the phenomenon of chunking in cognitive psychology as a memory mechanism for improving working memory efficiency, we proposed a one-dimensional block local binary pattern (1D-BlockLBP) encoding scheme combined with HDC to preserve dominant dynamical and temporal characteristics of pulse rate signals from wearable PPG devices. RESULTS: Our method achieved 70.17% accuracy in sleep apnea segment detection, which is comparable with traditional machine learning methods. Additionally, our method achieves up to 67× lower memory footprint, 68× latency reduction, and 93× energy saving on the ARM Cortex-M4 processor. CONCLUSION: The simplicity of hypervector operations in HDC and the novel 1D-BlockLBP encoding effectively preserve pulse rate signal characteristics with high computational efficiency. SIGNIFICANCE: This work provides a scalable solution for long-term home-based monitoring of sleep apnea, enhancing the feasibility of consistent patient care.

Open-Source Instrumented Object to Study Dexterous Object Manipulation.

Humans use tactile feedback to perform skillful manipulation. When tactile sensory feedback is unavailable, for instance, if the fingers are anesthetized, dexterity is severely impaired. Imaging the deformation of the finger pad skin when in contact with a transparent plate provides information about the tactile feedback received by the central nervous system. Indeed, skin deformations are transduced into neural signals by the mechanoreceptors of the finger pad skin. Understanding how this feedback is used for active object manipulation would improve our understanding of human dexterity. In this paper, we present a new device for imaging the skin of the finger pad of one finger during manipulation performed with a precision grip. The device's mass (300 g) makes it easy to use during unconstrained dexterous manipulation. Using this device, we reproduced the experiment performed in Delhaye et al. (2021) We extracted the strains aligned with the object's movement, i.e., the vertical strains in the ulnar and radial parts of the fingerpad, to see how correlated they were with the grip force (GF) adaptation. Interestingly, parts of our results differed from those in Delhaye et al. (2021) due to weight and inertia differences between the devices, with average GF across participants differing significantly. Our results highlight a large variability in the behavior of the skin across participants, with generally low correlations between strain and GF adjustments, suggesting that skin deformations are not the primary driver of GF adaptation in this manipulation scenario.

Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements.

BACKGROUND: Cardiac output (CO) and systemic vascular resistance (SVR) are two important parameters of the cardiovascular system. The ability to measure these parameters continuously and noninvasively may assist in diagnosing and monitoring patients with suspected cardiovascular diseases, or other critical illnesses. In this study, a method is proposed to estimate both the CO and SVR of a heterogeneous cohort of intensive care unit patients (N=48). METHODS: Spectral and morphological features were extracted from the finger photoplethysmogram, and added to heart rate and mean arterial pressure as input features to a multivariate regression model to estimate CO and SVR. A stepwise feature search algorithm was employed to select statistically significant features. Leave-one-out cross validation was used to assess the generalized model performance. The degree of agreement between the estimation method and the gold standard was assessed using Bland-Altman analysis. RESULTS: The Bland-Altman bias ±precision (1.96 times standard deviation) for CO was -0.01 ±2.70 L min-1 when only photoplethysmogram (PPG) features were used, and for SVR was -0.87 ±412 dyn.s.cm-5 when only one PPG variability feature was used. CONCLUSIONS: These promising results indicate the feasibility of using the method described as a non-invasive preliminary diagnostic tool in supervised or unsupervised clinical settings.

Biomechanics of the finger pad in response to torsion.

Surface skin deformation of the finger pad during partial slippage at finger-object interfaces elicits firing of the tactile sensory afferents. A torque around the contact normal is often present during object manipulation, which can cause partial rotational slippage. Until now, studies of surface skin deformation have used stimuli sliding rectilinearly and tangentially to the skin. Here, we study surface skin dynamics under pure torsion of the right index finger of seven adult participants (four males). A custom robotic platform stimulated the finger pad with a flat clean glass surface, controlling the normal forces and rotation speeds applied while monitoring the contact interface using optical imaging. We tested normal forces between 0.5 N and 10 N at a fixed angular velocity of 20° s-1 and angular velocities between 5° s-1 and 100° s-1 at a fixed normal force of 2 N. We observe the characteristic pattern by which partial slips develop, starting at the periphery of the contact and propagating towards its centre, and the resulting surface strains. The 20-fold range of normal forces and angular velocities used highlights the effect of those parameters on the resulting torque and skin strains. Increasing normal force increases the contact area, the generated torque, strains and the twist angle required to reach full slip. On the other hand, increasing angular velocity causes more loss of contact at the periphery and higher strain rates (although it has no impact on resulting strains after the full rotation). We also discuss the surprisingly large inter-individual variability in skin biomechanics, notably observed in the twist angle the stimulus needs to rotate before reaching full slip.

Automatic Segmentation of the Paediatric Femoral Head.

Developmental dysplasia of the hip (DDH) is a developmental deformity occurring in 0.1-3.4% of infants. Timely surgical intervention can ameliorate the condition in stable hips and reduce future cases of osteoarthritis and total hip replacement. However, current definitions of DDH are subjective, and thus would benefit from a more objective and reliable assessment metric. Since the shape of the femoral head and its congruence with the acetabulum are disrupted by DDH, analysis of the femoral head could potentially play a role in the development of novel objective morphological metric for stable DDH. Therefore, this paper aimed to segment the paediatric femoral head in stable hips from radiographs, which has not been attempted before in the chosen focus age group (1-16 years) where the pelvis and hip joint undergo significant development. Two techniques were compared against a baseline U-Net: data augmentation and region-of-interest (ROI) networks. Four models were developed either without, with just one, or with both techniques. Evaluated using tenfold cross-validation, the U-Net trained with both techniques achieved the best results, with a Dice Similarity Coefficient (DSC) of 0.951±0.037 (mean ± standard deviation, calculated with 720 images). Future work will use this segmentation algorithm to accurately characterise hip joint morphology and estimate the benefit of early surgical intervention in DDH.

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.

A Smartphone-Based Model of Care to Support Patients With Cardiac Disease Transitioning From Hospital to the Community (TeleClinical Care): Pilot Randomized Controlled Trial.

BACKGROUND: Patients hospitalized with acute coronary syndrome (ACS) or heart failure (HF) are frequently readmitted. This is the first randomized controlled trial of a mobile health intervention that combines telemonitoring and education for inpatients with ACS or HF to prevent readmission. OBJECTIVE: This study aims to investigate the feasibility, efficacy, and cost-effectiveness of a smartphone app-based model of care (TeleClinical Care [TCC]) in patients discharged after ACS or HF admission. METHODS: In this pilot, 2-center randomized controlled trial, TCC was applied at discharge along with usual care to intervention arm participants. Control arm participants received usual care alone. Inclusion criteria were current admission with ACS or HF, ownership of a compatible smartphone, age ≥18 years, and provision of informed consent. The primary end point was the incidence of unplanned 30-day readmissions. Secondary end points included all-cause readmissions, cardiac readmissions, cardiac rehabilitation completion, medication adherence, cost-effectiveness, and user satisfaction. Intervention arm participants received the app and Bluetooth-enabled devices for measuring weight, blood pressure, and physical activity daily plus usual care. The devices automatically transmitted recordings to the patients' smartphones and a central server. Thresholds for blood pressure, heart rate, and weight were determined by the treating cardiologists. Readings outside these thresholds were flagged to a monitoring team, who discussed salient abnormalities with the patients' usual care providers (cardiologists, general practitioners, or HF outreach nurses), who were responsible for further management. The app also provided educational push notifications. Participants were followed up after 6 months. RESULTS: Overall, 164 inpatients were randomized (TCC: 81/164, 49.4%; control: 83/164, 50.6%; mean age 61.5, SD 12.3 years; 130/164, 79.3% men; 128/164, 78% admitted with ACS). There were 11 unplanned 30-day readmissions in both groups (P=.97). Over a mean follow-up of 193 days, the intervention was associated with a significant reduction in unplanned hospital readmissions (21 in TCC vs 41 in the control arm; P=.02), including cardiac readmissions (11 in TCC vs 25 in the control arm; P=.03), and higher rates of cardiac rehabilitation completion (20/51, 39% vs 9/49, 18%; P=.03) and medication adherence (57/76, 75% vs 37/74, 50%; P=.002). The average usability rating for the app was 4.5/5. The intervention cost Aus $6028 (US $4342.26) per cardiac readmission saved. When modeled in a mainstream clinical setting, enrollment of 237 patients was projected to have the same expenditure compared with usual care, and enrollment of 500 patients was projected to save approximately Aus $100,000 (approximately US $70,000) annually. CONCLUSIONS: TCC was feasible and safe for inpatients with either ACS or HF. The incidence of 30-day readmissions was similar; however, long-term benefits were demonstrated, including fewer readmissions over 6 months, improved medication adherence, and improved cardiac rehabilitation completion. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12618001547235; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=375945.

A review of the neurobiomechanical processes underlying secure gripping in object manipulation.

O'SHEA, H. and S. J. Redmond. A review of the neurobiomechanical processes underlying secure gripping in object manipulation. NEUROSCI BIOBEHAV REV 286-300, 2021. Humans display skilful control over the objects they manipulate, so much so that biomimetic systems have yet to emulate this remarkable behaviour. Two key control processes are assumed to facilitate such dexterity: predictive cognitive-motor processes that guide manipulation procedures by anticipating action outcomes; and reactive sensorimotor processes that provide important error-based information for movement adaptation. Notwithstanding increased interdisciplinary research interest in object manipulation behaviour, the complexity of the perceptual-sensorimotor-cognitive processes involved and the theoretical divide regarding the fundamentality of control mean that the essential mechanisms underlying manipulative action remain undetermined. In this paper, following a detailed discussion of the theoretical and empirical bases for understanding human dexterous movement, we emphasise the role of tactile-related sensory events in secure object handling, and consider the contribution of certain biophysical and biomechanical phenomena. We aim to provide an integrated account of the current state-of-art in skilled human-object interaction that bridges the literature in neuroscience, cognitive psychology, and biophysics. We also propose novel directions for future research exploration in this area.