An MRI-based leg model used to simulate biomechanical phenomena during cuff algometry: a finite element study

Estimation of Pressure Pain in the Lower Limbs Using Electrodermal Activity, Tissue Oxygen Saturation, and Heart Rate Variability

Quantification of pain or discomfort induced by pressure is essential for understanding human responses to physical stimuli and improving user interfaces. Pain research has been conducted to investigate physiological signals associated with discomfort and pain perception. This study analyzed changes in electrodermal activity (EDA), tissue oxygen saturation (StO2), heart rate variability (HRV), and Visual Analog Scale (VAS) under pressures of 10, 20, and 30 kPa applied for 3 min to the thigh, knee, and calf in a seated position. Twenty participants were tested, and relationships between biosignals, pressure intensity, and pain levels were evaluated using Friedman tests and post-hoc analyses. Multiple linear regression models were used to predict VAS and pressure, and five machine learning models (SVM, Logistic Regression, Random Forest, MLP, KNN) were applied to classify pain levels (no pain: VAS 0, low: VAS 1-3, moderate: VAS 4-6, high: VAS 7-10) and pressure intensity. The results showed that higher pressure intensity and pain levels affected sympathetic nervous system responses and tissue oxygen saturation. Most EDA features and StO2 significantly changed according to pressure intensity and pain levels, while NN interval and HF among HRV features showed significant differences based on pressure intensity or pain level. Regression analysis combining biosignal features achieved a maximum R2 of 0.668 in predicting VAS and pressure intensity. The four-level classification model reached an accuracy of 88.2% for pain levels and 81.3% for pressure intensity. These results demonstrated the potential of EDA, StO2, HRV signals, and combinations of biosignal features for pain quantification and prediction.

Template models for simulation of surface manipulation of musculoskeletal extremities

Capturing the surface mechanics of musculoskeletal extremities would enhance the realism of life-like mechanics imposed on the limbs within surgical simulations haptics. Other fields that rely on surface manipulation, such as garment or prosthetic design, would also benefit from characterization of tissue surface mechanics. Eight homogeneous tissue models were developed for the upper and lower legs and arms of two donors. Ultrasound indentation data was used to drive an inverse finite element analysis for individualized determination of region-specific material coefficients for the lumped tissue. A novel calibration strategy was implemented by using a ratio based adjustment of tissue properties from linear regression of model predicted and experimental responses. This strategy reduced requirement of simulations to an average of under four iterations. These free and open-source specimen-specific models can serve as templates for simulations focused on mechanical manipulations of limb surfaces.

Psychophysical predictors of experimental muscle pain intensity following fatiguing calf exercise

Musculoskeletal pain affects approximately 20% of the population worldwide and represents one of the leading causes of global disability. As yet, precise mechanisms underlying the development of musculoskeletal pain and transition to chronicity remain unclear, though individual factors such as sleep quality, physical activity, affective state, pain catastrophizing and psychophysical pain sensitivity have all been suggested to be involved. This study aimed to investigate whether factors at baseline could predict musculoskeletal pain intensity to an experimental delayed onset of muscle soreness (DOMS) pain model. Demographics, physical activity, pain catastrophizing, affective state, sleep quality, isometric force production, temporal summation of pain, and psychophysical pain sensitivity using handheld and cuff algometry were assessed at baseline (Day-0) and two days after (Day-2) in 28 healthy participants. DOMS was induced on Day-0 by completing eccentric calf raises on the non-dominant leg to fatigue. On Day-2, participants rated pain on muscle contraction (visual analogue scale, VAS, 0-10cm) and function (Likert scale, 0–6). DOMS resulted in non-dominant calf pain at Day-2 (3.0±2.3cm), with significantly reduced isometric force production (P<0.043) and handheld pressure pain thresholds (P<0.010) at Day-2 compared to Day-0. Linear regression models using backward selection predicted from 39.3% (P<0.003) of VAS to 57.7% (P<0.001) of Likert score variation in DOMS pain intensity and consistently included cuff pressure pain tolerance threshold (P<0.01), temporal summation of pain (P<0.04), and age (P<0.02) as independent predictive factors. The findings indicate that age, psychological and central pain mechanistic factors are consistently associated with pain following acute muscle injury.

Controlled manual loading of body tissues: towards the next generation of pressure algometer

Assessing the responses of body tissue subjected to mechanical load is a fundamental component of the clinical examination, psychophysical assessments and bioengineering research. The forces applied during such assessments are usually generated manually, via the hands of the tester, and aimed at discreet tissue sites. It is therefore desirable to objectively quantify and optimise the control of manually applied force. However, current laboratory-grade manual devices and commercial software packages, in particular pressure algometer systems, are generally inflexible and expensive. This paper introduces and discusses several principles that should be implemented as design goals within a flexible, generic software application, given currently available force measurement hardware. We also discuss pitfalls that clinicians and researchers might face when using current pressure algometer systems and provide examples of these. Finally, we present our implementation of a pressure algometer system that achieves these goals in an efficient and affordable way for researchers and clinicians. As part of this effort, we will be sharing our configurable software application via a software repository.

Cuff Pressure Algometry in Patients with Chronic Pain as Guidance for Circumferential Tissue Compression for Wearable Soft Exoskeletons: A Systematic Review

In this article, we report on a systematic review of the literature on pressure-pain thresholds induced and assessed by computerized cuff pressure algometry (CPA). The motivation for this review is to provide design guidance on pressure levels for wearable soft exoskeletons and similar wearable robotics devices. In our review, we focus on CPA studies of patients who are candidates for wearable soft exoskeletons, as pain-related physiological mechanisms reportedly differ significantly between healthy subjects and patients with chronic pain. The results indicate that circumferential limb compression in patients most likely becomes painful at ∼10-18 kPa and can become unbearable even below 25 kPa. The corresponding ranges for healthy control subjects are 20-42 kPa (painful limits) and 34-84 kPa (unbearable levels). In addition, the increase of pain with time tends to be significantly higher, and the adaptation to pain significantly lower, than in healthy subjects. The results of this review provide guidance to designers of wearable robotics for populations with chronic pain regarding rates and magnitudes of tissue compression that may be unacceptable to users.

Computerized Cuff Pressure Algometry as Guidance for Circumferential Tissue Compression for Wearable Soft Robotic Applications: A Systematic Review

In this article, we review the literature on quantitative sensory testing of deep somatic pain by means of computerized cuff pressure algometry (CPA) in search of pressure-related safety guidelines for wearable soft exoskeleton and robotics design. Most pressure-related safety thresholds to date are based on interface pressures and skin perfusion, although clinical research suggests the deep somatic tissues to be the most sensitive to excessive loading. With CPA, pain is induced in deeper layers of soft tissue at the limbs. The results indicate that circumferential compression leads to discomfort at ∼16-34 kPa, becomes painful at ∼20-27 kPa, and can become unbearable even below 40 kPa.

Tissue mechanics during temporal summation of sequentially cuff pressure-induced pain in healthy volunteers and patients with painful osteoarthritis

BACKGROUND

The phenomenon of temporal summation to repetitive pressure pain stimuli is an important central neural mechanism for pain intensity encoding. This study evaluated the time-dependent behaviour of mechanical characteristics of soft tissue during repeated cuff stimulation used for eliciting temporal summation of cuff pressure-evoked pain. Such information of tissue mechanics is important for the interpretation of the pain response evoked during sequential stimulations.

METHODS

Temporal summation was assessed in 16 subjects separated into two groups (healthy controls and severe knee osteoarthritis patients) using a visual analogue scale during 10 repetitive painful cuff stimuli (1-s duration, 1-s break) of the lower leg. The geometry of the lower leg was constructed based on magnetic resonance image (MRI) data. The loading boundary condition of the finite element model was defined according to the parabolic pattern of the interface pressure around the limb and the time-dependent profile of the cuff pressure during repetitive stimuli.

RESULTS

The pain intensity significantly increased with an increasing number of stimuli (p < 0.001), and facilitated temporal summation of pain was observed in patients compared with healthy controls (p < 0.001). The maximal deep tissue stress and strain during stimuli 1-4 varied 43% and 9%, respectively. No variation was observed for stimuli 5-10.

CONCLUSIONS

The study concludes that the temporal summation of pain response during sequential cuff pressure is not explicable by a specific time-dependent behaviour of stress and strain in the activated deep tissue and hence not due to changes in tissue biomechanics.

SIGNIFICANCE

The temporal summation of pain during sequential cuff stimulation is inexplicable by the time-dependent response of mechanical stress and strain in soft tissue.