Real-Time-Capable Muscle Force Estimation for Monitoring Robotic Rehabilitation Therapy in the Intensive Care Unit.

In this paper, a method is proposed to enable real-time monitoring of muscle forces during robotic rehabilitation therapy in the ICU. This method is solely based on sensor information provided by the rehabilitation robot. In current clinical practice, monitoring primarily takes place in the later stages of rehabilitation, but it would also be highly beneficial during early stages. Musculoskeletal models have large, mostly unrealized potential to support and improve patient monitoring. The method presented in this paper is based on a state-of-the-art muscle-tendon path model, which is applied to the use case of the robotic rehabilitation device VEMOTION. The muscle force estimation is validated against surface electromyography measurements of lower limb muscles from 12 healthy volunteers The results show an overall correlation of R = 0.70 0.25 for the single-joint muscle m. iliopsoas, which has a ±major contribution to hip flexion. Given this correlation, the proposed model could be used for real-time monitoring of active patient participation.

Estimating Joint Kinematics and Muscles Forces During Robotic Rehabilitation to Detect and Counteract Reduced Ankle Mobility.

The paper presents a solution to detect active ankle joint movement while a patient undergoes therapy with a robotic lower limb rehabilitation device that neither restricts nor actively supports ankle dorsi- or plantarflexion. The presented method requires the addition of only two accelerometer sensors to the system as well as a musculoskeletal model of the lower limb. Using forward kinematics and inverse dynamics, it enables knee and ankle joint kinematic tracking in the sagittal plane and muscle force estimation. This is an extension of a previous work in which only hip joint tracking was possible and, thus, muscle force estimation was limited. The correlation results of the current validation study with 12 healthy subjects show high correlation (R=0.88±0.09) between the kinematics estimated with the proposed method and those calculated from a gold standard motion capture setup for all three joints (hip, knee, and ankle). The correlation results of the estimated m. tibialis anterior muscle force against electromyography measurements (R = 0.62±0.27) are promising and a first application to a patient data set shows potential for future clinical application.

Monitoring Active Patient Participation During Robotic Rehabilitation: Comparison Between a Robot-Based Metric and an EMG-Based Metric.

While rehabilitation robots present a much-needed solution to improving early mobilization therapy in demanding clinical settings, they also present new challenges and opportunities in patient monitoring. Aside from the fundamental challenge of quantifying a patient's voluntary contribution during robot-led therapy motion, many sensors cannot be used in clinical settings due to time and space limitations. In this paper, we present and compare two metrics for monitoring a patient's active participation in the motion. The two metrics, each derived from first principles, have the same biomechanical interpretability, i.e., active work by the patient during the robotic mobilization therapy, but are calculated in two different spaces (Cartesian vs. muscle space). Furthermore, the sensors used to quantify these two metrics are fully independent from each other and the associated measurements are unrelated. Specifically, the robot-based work metric utilizes robot-integrated force sensors, while the EMG-based work metric requires electrophysiological sensors. We then apply the two metrics to therapy performed using a clinically certified, commercially available robotic system and compare them against the specific instructions given to the healthy subjects as well as against each other. Both metric outputs qualitatively match the expected behavior of the healthy subjects. Additionally, strong correlations (median [Formula: see text]) are shown between the two metrics, not only for healthy subjects (n = 12) but also for patients (n = 2), providing solid evidence for their validity and translatability. Importantly, the robot-based work metric does not rely on any sensors outside of those integrated into the robot, thus making it ideal for application in clinical settings.

Testing robot-based assist-as-needed therapy for improving active participation of a patient during early neurorehabilitation: a case study.

In this study, a patient in the Intensive Care-Unit received robot-based mobilization therapy with an assist-as-needed (AAN) function over the course of three weeks. Therapists were able to adapt the hip range of motion $\beta$, the bed verticalization angle $\alpha$ and the leg load force FLoad for each therapy, based on the current condition of the patient. To evaluate the patient active participation, surface electromyography (sEMG) of the M. rectus femoris (RF) and M. biceps femoris (BF) were measured and analyzed. It was observed that the patient active participation, measured through sEMG, increased along with increased hip range of motion $\beta$, bed verticalization angle $\alpha$ and leg load force FLoad set by the therapists. The patient muscle activation pattern followed the pattern of healthy controls, in part. To the authors' best knowledge, this study is the first of its kind to be performed with an ICU patient.

Design Considerations of a Fiber Optic Pressure Sensor Protective Housing for Intramuscular Pressure Measurements.

Intramuscular pressure (IMP), defined as skeletal muscle interstitial fluid pressure, reflects changes in individual muscle tension and may provide crucial insight into musculoskeletal biomechanics and pathologies. IMP may be measured using fiber-optic fluid pressure sensors, provided the sensor is adequately anchored to and shielded from surrounding muscle tissue. Ineffective anchoring enables sensor motion and inadequate shielding facilitates direct sensor-tissue interaction, which result in measurement artifacts and force-IMP dissociation. The purpose of this study was to compare the effectiveness of polyimide and nitinol protective housing designs to anchor pressure sensors to muscle tissue, prevent IMP measurement artifacts, and optimize the force-IMP correlation. Anchoring capacity was quantified as force required to dislodge sensors from muscle tissue. Force-IMP correlations and non-physiological measurement artifacts were quantified during isometric muscle activations of the rabbit tibialis anterior. Housing structural integrity was assessed after both anchoring and activation testing. Although there was no statistically significant difference in anchoring capacity, nitinol housings demonstrated greater structural integrity and superior force-IMP correlations. Further design improvements are needed to prevent tissue accumulation in the housing recess associated with artificially high IMP measurements. These findings emphasize fundamental protective housing design elements crucial for achieving reliable IMP measurements.

Characterization of three dimensional volumetric strain distribution during passive tension of the human tibialis anterior using Cine Phase Contrast MRI.

Intramuscular pressure correlates strongly with muscle tension and is a promising tool for quantifying individual muscle force. However, clinical application is impeded by measurement variability that is not fully understood. Previous studies point to regional differences in IMP, specifically increasing pressure with muscle depth. Based on conservation of mass, intramuscular pressure and volumetric strain distributions may be inversely related. Therefore, we hypothesized volumetric strain would decrease with muscle depth. To test this we quantified 3D volumetric strain in the tibialis anterior of 12 healthy subjects using Cine Phase Contrast Magnetic Resonance Imaging. Cine Phase Contrast data were collected while a custom apparatus rotated the subjects' ankle continuously between neutral and plantarflexion. A T2-weighted image stack was used to define the resting tibials anterior position. Custom and commercial post-processing software were used to quantify the volumetric strain distribution. To characterize regional strain changes, the muscle was divided into superior-inferior sections and either medial-lateral or anterior-posterior slices. Mean volumetric strain was compared across the sections and slices. As hypothesized, volumetric strain demonstrated regional differences with a decreasing trend from the anterior (superficial) to the posterior (deep) muscle regions. Statistical tests showed significant main effects and interactions of superior-inferior and anterior-posterior position as well as superior-inferior and medial-lateral position on regional strain. These data support our hypothesis and imply a potential relationship between regional volumetric strain and intramuscular pressure. This finding may advance our understanding of intramuscular pressure variability sources and lead to more reliable measurement solutions in the future.

Method of quantifying 3D strain distribution in skeletal muscle using cine phase contrast MRI.

Intramuscular pressure (IMP), a correlate of muscle tension, may fill an important clinical testing void. A barrier to implementing this measure clinically is its non-uniform distribution, which is not fully understood. Pressure is generated by changes in fluid mass and volume, therefore 3D volumetric strain distribution may affect IMP distribution. The purpose of this study was to develop a method for quantifying 3D volumetric strain distribution in the human tibialis anterior (TA) during passive tension using cine phase contrast (CPC) MRI and to assess its accuracy and precision.Five healthy subjects each participated in three data collections. A custom MRI-compatible apparatus repeatedly rotated a subject's ankle between 0° and 26° plantarflexion while CPC MRI data were collected. Additionally, T2-weighted images of the lower leg were collected both before and after the CPC data collection with the ankle stationary at both 0° and 26° plantarflexion for TA muscle segmentation. A 3D hexahedral mesh was generated based on the TA surface before CPC data collection with the ankle at 0° plantarflexion and the node trajectories were tracked using the CPC data. The volumetric strain of each element was quantified.Three tests were employed to assess the measure accuracy and precision. First, to quantify leg position drift, the TA segmentations were compared before and after CPC data collection. The Hawsdorff distance measure (error) was 1.5 ± 0.7 mm. Second, to assess the surface node trajectory accuracy, the deformed mesh surface was compared to the TA segmented at 26° of ankle plantarflexion. This error was 0.6 ± 0.2 mm. Third, the standard deviation of volumetric strain across the three data collections was calculated for each element and subject. The median between-day variability across subjects and mesh elements was 0.06 mm3 mm(-3) (95% confidence interval 0.01 to 0.18 mm3 mm(-3)). Overall the results demonstrated excellent accuracy and precision.

Error analysis of cine phase contrast MRI velocity measurements used for strain calculation.

Cine Phase Contrast (CPC) MRI offers unique insight into localized skeletal muscle behavior by providing the ability to quantify muscle strain distribution during cyclic motion. Muscle strain is obtained by temporally integrating and spatially differentiating CPC-encoded velocity. The aim of this study was to quantify CPC measurement accuracy and precision and to describe error propagation into displacement and strain. Using an MRI-compatible jig to move a B-gel phantom within a 1.5 T MRI bore, CPC-encoded velocities were collected. The three orthogonal encoding gradients (through plane, frequency, and phase) were evaluated independently in post-processing. Two systematic error types were corrected: eddy current-induced bias and calibration-type error. Measurement accuracy and precision were quantified before and after removal of systematic error. Through plane- and frequency-encoded data accuracy were within 0.4 mm/s after removal of systematic error - a 70% improvement over the raw data. Corrected phase-encoded data accuracy was within 1.3 mm/s. Measured random error was between 1 to 1.4 mm/s, which followed the theoretical prediction. Propagation of random measurement error into displacement and strain was found to depend on the number of tracked time segments, time segment duration, mesh size, and dimensional order. To verify this, theoretical predictions were compared to experimentally calculated displacement and strain error. For the parameters tested, experimental and theoretical results aligned well. Random strain error approximately halved with a two-fold mesh size increase, as predicted. Displacement and strain accuracy were within 2.6 mm and 3.3%, respectively. These results can be used to predict the accuracy and precision of displacement and strain in user-specific applications.