Validation of ankle strength measurements by means of a hand-held dynamometer in adult healthy subjects

Uniaxial Hand-Held Dynamometer (HHD) is a low-cost device widely adopted in clinical practice to measure muscle force. HHD measurements depend on operator’s ability and joint movements. The aim of the work is to validate the use of a commercial HHD in both dorsiflexion and plantarflexion ankle strength measurements quantifying the effects of HHD misplacements and unwanted foot’s movements on the measurements. We used an optoelectronic system and a multicomponent load cell to quantify the sources of error in the manual assessment of the ankle strength due to both the operator’s ability to hold still the HHD and the transversal components of the exerted force that are usually neglected in clinical routine. Results showed that foot’s movements and angular misplacements of HHD on sagittal and horizontal planes were relevant sources of inaccuracy on the strength assessment. Moreover, ankle dorsiflexion and plantarflexion force measurements presented an inaccuracy less than 2% and higher than 10%, respectively. In conclusion, the manual use of a uniaxial HHD is not recommend ed for the assessment of ankle plantarflexion strength; on the contrary, it can be allowed asking the operator to pay strong attention to the HHD positioning in ankle dorsiflexion strength measurements

Stereophotogrammetry in human movement analysis: novel methods for the quality assurance, biomechanical analysis and clinical interpretation of gait analysis

The study of movement has always fascinated artists, photographers and researchers. Across the years, several attempts to capture, freeze, study and reproduce motion were made. Nowadays, motion capture plays an important role within many fields, from graphical animation, filmmaking, virtual reality, till medicine. In fact, movement analysis allows to measure kinematic and kinetic performance of the human body. The quantitative data obtained from measurements may support the diagnosis and treatment of many pathologies, allowing to take clinical decisions and supporting the follow-up of treatments or rehabilitation. This approach is nowadays named evidence based medicine. In this work, motion capture techniques and advanced signal processing techniques were exploited in order to: (i) develop a protocol for the validation and quality assurance of the clinical strength measurements, (ii) develop an algorithm for clinical gait analysis data interpretation and identification of pathological patterns, and (iii) design user-friendly software tools to help clinicians using the novel data processing algorithms and reporting the results of measurements. This work was divided into three sections: Part 1 contains a survey about the history of motion analysis and a review of the earliest experiments in biomechanics. The review covered the first historical attempts, that were mainly based on photography, till the state-of-the-art technology used today, i.e. the optoelectronic system. The working principle of optoelectronic system was reviewed as well as its applications and modern setups in the clinical practice. Some modern functional evaluation protocols, aimed to the quantitative evaluation of physical performance and clinical diagnosis of motor disorders, were also reviewed. Special attention was paid to the most common motion analysis exam that is nowadays worldwide standardized, i.e. the Gait Analysis. Examples of Gait Analysis studies on subjects with pathology and follow-up were reviewed. Part 2 concerns the design of an experimental setup, involving motion analysis, for the quality assurance of clinical strength measurements. Measurements of force are popular in the clinical practice as they allow to evaluate the muscle weakness, health status of patients and the effects of therapies. A variety of protocols was proposed to conduct such measurements, implying the acquisition of forces, angles and angular velocities when the maximum voluntary force is exerted. Hand held dynamometry (HHD), based on single component load cell, was extensively used in clinical practice; however, several shortcomings were identified. The most relevant were related to the operator’s ability. This work was aimed to investigate the inherent inaccuracy sources in knee strength measurements when are conducted by a single component load cell. The analysis was conducted by gathering the outputs of a compact six-component load cell, comparable in dimension and mass to clinical HHDs, and an optoelectronic system. Quality of measurements was investigated in terms of quantifying, by an ad-hoc metrics, the effects induced in the overall inaccuracy by: (i) the operator’s ability to place and to hold still the HHD and (ii) ignoring the transversal components of the force exchanged between the patient and the experimenter. The main finding was that the use of a single component HHD induced an overall inaccuracy of 5% in the strength measurements, when operated by a trained clinician and angular misplacements are kept within the values found in this work (≤15°) and with a knee ROM ≤ 22°. Even if the measurement outputs were reliable and accurate enough for both knee flexion and extension, extension trials were the most critical due to the higher force exerted, i.e. 249.4±27.3 N vs. 146.4±23.9 N of knee flexion. The most relevant source of inaccuracy was identified in the angular displacement of HHD on the horizontal plane. A dedicated software, with graphical user interface, was designed and implemented. The purposes of this software were to: (i) speed up data processing, (ii) allow user to select the proper processing workflow, and (iii) provide clinicians with a tool for quick data processing and reporting. Part 3 concerns the research study about gait analysis on subjects with pathology. Gait analysis is often used for the assessment of the gait abilities in children with cerebral palsy and to quantify improvements/variations after a treatment. To simplify GA interpretation and to quantify deviation from normality, some synthetic descriptors were developed in literature, such as the Movement Analysis Profile (MAP) and the Linear Fit Method (LFM). The aims of this work were: (i) to use synthetic descriptors in order to quantify gait variations in subjects with Cerebral Palsy that underwent surgery involving bone repositioning and muscle/tendon lengthening at the level of the femur and hamstring group (SEMLS); (ii) test the effectiveness of a recently proposed index, i.e. the LFM, on such patients; (iii) design and implement a novel index that may overcome the limitations of the previous methods. Gait Analysis exams of 10 children with Cerebral Palsy, pre and post treatment, were collected. Data were analysed by means of MAP and LFM indices. To overcome the limitations observed for the methods, another index was designed as a modified version of the MAP, namely the OC-MAP. It took into account the effect on deviation due to offset and allowed to compute the deviation from normality on tracks purified by the offset. An overall improvement of the gait pattern was observed for most of the subjects after surgery. The highest effect was observed for the knee flexion/extension angle. Patients who had initial high deviations also had the largest improvements. Worsening in the kinematics of the pelvis could be explained as a consequence of SEML involving a lengthening of hamstring group. Pre-post differences were higher than the Minimally Clinical Important Difference for all parameters, except hip flexion. An improvement towards normality was observed for all the parameters, with exception of pelvic tilt for which a worsening was observed. LFM provided results similar to OC-MAP offset analysis but could not be considered reliable due to intrinsic limitations. As offset in gait features played an important role in gait deviation, OC-MAP synthetic analysis is recommended to study gait pattern of subjects with Cerebral Palsy. A dedicated software, with graphical user interface, was designed and implemented. The purpose of this software was to compute the synthetic descriptors on a large amount of data, to speedup data processing and to provide clinicians with a quick access to the result

Handwriting style classification

This paper describes an independent handwriting style classifier that has been designed to select the best recognizer for a given style of writing. For this purpose a definition of handwriting legibility has been defined and a method implemented that can predict this legibility. The technique consists of two phases. In the feature-extraction phase, a set of 36 features is extracted from the image contour. In the classification phase, two nonparametric classification techniques are applied to the extracted features in order to compare their effectiveness in classifying words into legible, illegible, and middle classes. In the first method, a multiple discriminant analysis (MDA) is used to transform the space of extracted features (36 dimensions) into an optimal discriminant space for a nearest mean based classifier. In the second method, a probabilistic neural network (PNN) based on the Bayes strategy and nonparametric estimation of probability density function is used. The experimental results show that the PNN method gives superior classification results when compared with the MDA method. For the legible, illegible, and middle handwriting the method provides 86.5% (legible/illegible), 65.5% (legible/middle), and 90.5% (middle/illegible) correct classification for two classes. For the three-class legibility classification the rate of correct classification is 67.33% using a PNN classifier

Low-Cost Objective Measurement of Prehension Skills

This thesis aims to explore the feasibility of using low-cost, portable motion capture tools for the quantitative assessment of sequential 'reach-to-grasp' and repetitive 'finger-tapping' movements in neurologically intact and deficit populations, both in clinical and non-clinical settings. The research extends the capabilities of an existing optoelectronic postural sway assessment tool (PSAT) into a more general Boxed Infrared Gross Kinematic Assessment Tool (BIGKAT) to evaluate prehensile control of hand movements outside the laboratory environment. The contributions of this work include the validation of BIGKAT against a high-end motion capture system (Optotrak) for accuracy and precision in tracking kinematic data. BIGKAT was subsequently applied to kinematically resolve prehensile movements, where concurrent recordings with Optotrak demonstrate similar statistically significant results for five kinematic measures, two spatial measures (Maximum Grip Aperture – MGA, Peak Velocity – PV) and three temporal measures (Movement Time – MT, Time to MGA – TMGA, Time to PV – TPV). Regression analysis further establishes a strong relationship between BIGKAT and Optotrak, with nearly unity slope and low y-intercept values. Results showed reliable performance of BIGKAT and its ability to produce similar statistically significant results as Optotrak. BIGKAT was also applied to quantitatively assess bradykinesia in Parkinson's patients during finger-tapping movements. The system demonstrated significant differences between PD patients and healthy controls in key kinematic measures, paving the way for potential clinical applications. The study characterized kinematic differences in prehensile control in different sensory environments using a Virtual Reality head mounted display and finger tracking system (the Leap Motion), emphasizing the importance of sensory information during hand movements. This highlighted the role of hand vision and haptic feedback during initial and final phases of prehensile movement trajectory. The research also explored marker-less pose estimation using deep learning tools, specifically DeepLabCut (DLC), for reach-to-grasp tracking. Despite challenges posed by COVID-19 limitations on data collection, the study showed promise in scaling reaching and grasping components but highlighted the need for diverse datasets to resolve kinematic differences accurately. To facilitate the assessment of prehension activities, an Event Detection Tool (EDT) was developed, providing temporal measures for reaction time, reaching time, transport time, and movement time during object grasping and manipulation. Though initial pilot data was limited, the EDT holds potential for insights into disease progression and movement disorder severity. Overall, this work contributes to the advancement of low-cost, portable solutions for quantitatively assessing upper-limb movements, demonstrating the potential for wider clinical use and guiding future research in the field of human movement analysis

Low-loss photonic reservoir computing with multimode photonic integrated circuits

Abstract We present a numerical study of a passive integrated photonics reservoir computing platform based on multimodal Y-junctions. We propose a novel design of this junction where the level of adiabaticity is carefully tailored to capture the radiation loss in higher-order modes, while at the same time providing additional mode mixing that increases the richness of the reservoir dynamics. With this design, we report an overall average combination efficiency of 61% compared to the standard 50% for the single-mode case. We demonstrate that with this design, much more power is able to reach the distant nodes of the reservoir, leading to increased scaling prospects. We use the example of a header recognition task to confirm that such a reservoir can be used for bit-level processing tasks. The design itself is CMOS-compatible and can be fabricated through the known standard fabrication procedures

NASA Tech Briefs, October 2004

Topics include: Relative-Motion Sensors and Actuators for Two Optical Tables; Improved Position Sensor for Feedback Control of Levitation; Compact Tactile Sensors for Robot Fingers; Improved Ion-Channel Biosensors; Suspended-Patch Antenna With Inverted, EM-Coupled Feed; System Would Predictively Preempt Traffic Lights for Emergency Vehicles; Optical Position Encoders for High or Low Temperatures; Inter-Valence-Subband/Conduction-Band-Transport IR Detectors; Additional Drive Circuitry for Piezoelectric Screw Motors; Software for Use with Optoelectronic Measuring Tool; Coordinating Shared Activities; Software Reduces Radio-Interference Effects in Radar Data; Using Iron to Treat Chlorohydrocarbon-Contaminated Soil; Thermally Insulating, Kinematic Tensioned-Fiber Suspension; Back Actuators for Segmented Mirrors and Other Applications; Mechanism for Self-Reacted Friction Stir Welding; Lightweight Exoskeletons with Controllable Actuators; Miniature Robotic Submarine for Exploring Harsh Environments; Electron-Spin Filters Based on the Rashba Effect; Diffusion-Cooled Tantalum Hot-Electron Bolometer Mixers; Tunable Optical True-Time Delay Devices Would Exploit EIT; Fast Query-Optimized Kernel-Machine Classification; Indentured Parts List Maintenance and Part Assembly Capture Tool - IMPACT; An Architecture for Controlling Multiple Robots; Progress in Fabrication of Rocket Combustion Chambers by VPS; CHEM-Based Self-Deploying Spacecraft Radar Antennas; Scalable Multiprocessor for High-Speed Computing in Space; and Simple Systems for Detecting Spacecraft Meteoroid Punctures

Capturing the Cranio-Caudal Signature of a Turn with Inertial Measurement Systems: Methods, Parameters Robustness and Reliability

BACKGROUND: Turning is a challenging mobility task requiring coordination and postural stability. Optimal turning involves a cranio-caudal sequence (i.e., the head initiates the motion, followed by the trunk and the pelvis), which has been shown to be altered in patients with neurodegenerative diseases, such as Parkinson's disease as well as in fallers and frails. Previous studies have suggested that the cranio-caudal sequence exhibits a specific signature corresponding to the adopted turn strategy. Currently, the assessment of cranio-caudal sequence is limited to biomechanical labs which use camera-based systems; however, there is a growing trend to assess human kinematics with wearable sensors, such as attitude and heading reference systems (AHRS), which enable recording of raw inertial signals (acceleration and angular velocity) from which the orientation of the platform is estimated. In order to enhance the comprehension of complex processes, such as turning, signal modeling can be performed. AIM: The current study investigates the use of a kinematic-based model, the sigma-lognormal model, to characterize the turn cranio-caudal signature as assessed with AHRS. METHODS: Sixteen asymptomatic adults (mean age = 69.1 +/- 7.5 years old) performed repeated 10-m Timed-Up-and-Go (TUG) with 180 degrees turns, at varying speed. Head and trunk kinematics were assessed with AHRS positioned on each segments. Relative orientation of the head to the trunk was then computed for each trial and relative angular velocity profile was derived for the turn phase. Peak relative angle (variable) and relative velocity profiles modeled using a sigma-lognormal approach (variables: Neuromuscular command amplitudes and timing parameters) were used to extract and characterize the cranio-caudal signature of each individual during the turn phase. RESULTS: The methodology has shown good ability to reconstruct the cranio-caudal signature (signal-to-noise median of 17.7). All variables were robust to speed variations (p > 0.124). Peak relative angle and commanded amplitudes demonstrated moderate to strong reliability (ICC between 0.640 and 0.808). CONCLUSION: The cranio-caudal signature assessed with the sigma-lognormal model appears to be a promising avenue to assess the efficiency of turns