DYSKIMOT: An Ultra-Low-Cost Inertial Sensor to Assess Head's Rotational Kinematics in Adults during the Didren-Laser Test

Evaluating cervical spine mobility and Fitt's law compliance: The DidRen laser test adapted for virtual reality with age and sex effects

Cervical spine mobility assessment is crucial in rehabilitation to monitor patient progress. This study introduces the DidRen VR test, a virtual reality (VR) adaptation of the conventional DidRen laser test, aimed at evaluating cervical spine mobility. We conducted a cross-sectional study involving fifty healthy participants that underwent the DidRen VR test. The satisfaction of Fitts' law within this VR adaptation was examined and we analyzed the effects of age and sex on the sensorimotor performance metrics. Our findings confirm that Fitts' law is satisfied, demonstrating a predictable relationship between movement time and the index of difficulty, which suggest that the DidRen VR test can simulate real-world conditions. A clear influence of age and sex on performance was observed, highlighting significant differences in movement efficiency and accuracy across demographics, which may necessitate personalized assessment strategies in clinical rehabilitation practices. The DidRen VR test presents an effective tool for assessing cervical spine mobility, validated by Fitts' law. It offers a viable alternative to real-world method, providing precise control over test conditions and enhanced engagement for participants. Since age and sex significantly affect sensorimotor performance, personalized assessments are essential. Further research is recommended to explore the applicability of the DidRen VR test in clinical settings and among patients with neck pain.

Design and Characterization of a Wearable Inertial Measurement Unit

The utilization of inertial measurement units as wearable sensors is proliferating across various domains, such as health care, sports, and rehabilitation. This expansion has produced a market of devices tailored to accommodate very specific ranges of operational demands. Simultaneously, this growth is creating opportunities for the development of a new class of devices more oriented towards general-purpose use and capable of capturing both high-frequency signals for short-term, event-driven motion analysis and low-frequency signals for extended monitoring. For such a design, which combines flexibility and low cost, a rigorous evaluation of the device in terms of deviation, noise levels, and precision is essential. This evaluation is crucial for identifying potential improvements and refining the design accordingly, yet it is rarely addressed in the literature. This paper presents the development process of such a device. The results of the design process demonstrate acceptable performance in optimizing energy consumption and storage capacity while highlighting the most critical optimizations needed to advance the device towards the goal of a smart, general-purpose unit for human motion monitoring.

Validity of Inertial Measurement Unit (IMU Sensor) for Measurement of Cervical Spine Motion, Compared with Eight Optoelectronic 3D Cameras Under Spinal Immobilization Devices

Background

The assessment of cervical spine motion is critical for out-of-hospital patients who suffer traumatic spinal cord injuries, given the profound implications such injuries have on individual well-being and broader public health concerns. 3D Optoelectronic systems (BTS SmartDX) are standard devices for motion measurement, but their price, complexity, and size prevent them from being used outside of designated laboratories. This study was designed to evaluate the accuracy and reliability of an inertial measurement unit (IMU) in gauging cervical spine motion among healthy volunteers, using a 3D optoelectronic motion capture system as a reference.

Methods

Twelve healthy volunteers participated in the study. They underwent lifting, transferring, and tilting simulations using a long spinal board, a Sked stretcher, and a vacuum mattress. During these simulations, cervical spine angular movements-including flexion-extension, axial rotation, and lateral flexion-were concurrently measured using the IMU and an optoelectronic device. We employed the Wilcoxon signed-rank test and the Bland-Altman plot to assess reliability and validity.

Results

A single statistically significant difference was observed between the two devices in the flexion-extension plane. The mean differences across all angular planes ranged from -1.129° to 1.053°, with the most pronounced difference noted in the lateral flexion plane. Ninety-five percent of the angular motion disparities ascertained by the SmartDX and IMU were less than 7.873° for the lateral flexion plane, 11.143° for the flexion-extension plane, and 25.382° for the axial rotation plane.

Conclusion

The IMU device exhibited robust validity when assessing the angular motion of the cervical spine in the axial rotation plane and demonstrated commendable validity in both the lateral flexion and flexion-extension planes.

Use of Sensors for Fall Prediction in Older Persons: A Scoping Review

The application of technological advances and clear articulation of how they improve patient outcomes are not always well described in the literature. Our research team investigated the numerous ways to measure conditions and behaviors that precede patient events and could signal an important change in health through a scoping review. We searched for evidence of technology use in fall prediction in the population of older adults in any setting. The research question was described in the population-concept-context format: "What types of sensors are being used in the prediction of falls in older persons?" The purpose was to examine the numerous ways to obtain continuous measurement of conditions and behaviors that precede falls. This area of interest may be termed emerging knowledge . Implications for research include increased attention to human-centered design, need for robust research trials that clearly articulate study design and outcomes, larger sample sizes and randomization of subjects, consistent oversight of institutional review board processes, and elucidation of the human costs and benefits to health and science.

Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return Test

Nowadays, the better assessment of low back pain (LBP) is an important challenge, as it is the leading musculoskeletal condition worldwide in terms of years of disability. The objective of this study was to evaluate the relevance of various machine learning (ML) algorithms and Sample Entropy (SampEn), which assesses the complexity of motion variability in identifying the condition of low back pain. Twenty chronic low-back pain (CLBP) patients and 20 healthy non-LBP participants performed 1-min repetitive bending (flexion) and return (extension) trunk movements. Analysis was performed using the time series recorded by three inertial sensors attached to the participants. It was found that SampEn was significantly lower in CLBP patients, indicating a loss of movement complexity due to LBP. Gaussian Naive Bayes ML proved to be the best of the various tested algorithms, achieving 79% accuracy in identifying CLBP patients. Angular velocity of flexion movement was the most discriminative feature in the ML analysis. This study demonstrated that: supervised ML and a complexity assessment of trunk movement variability are useful in the identification of CLBP condition, and that simple kinematic indicators are sensitive to this condition. Therefore, ML could be progressively adopted by clinicians in the assessment of CLBP patients.

Head Pitch Angular Velocity Discriminates (Sub-)Acute Neck Pain Patients and Controls Assessed with the DidRen Laser Test

Understanding neck pain is an important societal issue. Kinematic data from sensors may help to gain insight into the pathophysiological mechanisms associated with neck pain through a quantitative sensorimotor assessment of one patient. The objective of this study was to evaluate the potential usefulness of artificial intelligence with several machine learning (ML) algorithms in assessing neck sensorimotor performance. Angular velocity and acceleration measured by an inertial sensor placed on the forehead during the DidRen laser test in thirty-eight acute and subacute non-specific neck pain (ANSP) patients were compared to forty-two healthy control participants (HCP). Seven supervised ML algorithms were chosen for the predictions. The most informative kinematic features were computed using Sequential Feature Selection methods. The best performing algorithm is the Linear Support Vector Machine with an accuracy of 82% and Area Under Curve of 84%. The best discriminative kinematic feature between ANSP patients and HCP is the first quartile of head pitch angular velocity. This study has shown that supervised ML algorithms could be used to classify ANSP patients and identify discriminatory kinematic features potentially useful for clinicians in the assessment and monitoring of the neck sensorimotor performance in ANSP patients.

Sample Entropy as a Tool to Assess Lumbo-Pelvic Movements in a Clinical Test for Low-Back-Pain Patients

Low back pain (LBP) obviously reduces the quality of life but is also the world’s leading cause of years lived with disability. Alterations in motor response and changes in movement patterns are expected in LBP patients when compared to healthy people. Such changes in dynamics may be assessed by the nonlinear analysis of kinematical time series recorded from one patient’s motion. Since sample entropy (SampEn) has emerged as a relevant index measuring the complexity of a given time series, we propose the development of a clinical test based on SampEn of a time series recorded by a wearable inertial measurement unit for repeated bending and returns (b and r) of the trunk. Twenty-three healthy participants were asked to perform, in random order, 50 repetitions of this movement by touching a stool and another 50 repetitions by touching a box on the floor. The angular amplitude of the b and r movement and the sample entropy of the three components of the angular velocity and acceleration were computed. We showed that the repetitive b and r “touch the stool” test could indeed be the basis of a clinical test for the evaluation of low-back-pain patients, with an optimal duration of 70 s, acceptable in daily clinical practice.

Head kinematics in patients with neck pain compared to asymptomatic controls: a systematic review

BACKGROUND

Neck pain is one of the most common musculoskeletal disorders encountered by healthcare providers. A precise assessment of functional deficits, including sensorimotor control impairment, is regarded necessary for tailored exercise programmes. Sensorimotor control can be measured by kinematic characteristics, such as velocity, acceleration, smoothness, and temporal measures, or by assessing movement accuracy. This systematic review aims to identify movement tasks and distinct outcome variables used to measure kinematics and movement accuracy in patients with neck pain and present their results in comparison to asymptomatic controls.

METHODS

Electronic searches were conducted in MEDLINE, PEDro, Cochrane Library and CINAHL databases from inception to August 2020. Risk of bias of included studies was assessed. Movement tasks and specific outcome parameters used were collated. The level of evidence for potential group differences in each outcome variable between patients with neck pain and controls was evaluated.

RESULTS

Twenty-seven studies examining head kinematics and movement accuracy during head-aiming, functional and unconstrained movement tasks of the head were included. Average Risk of Bias of included studies was moderate. In total, 23 different outcome variables were assessed. A strong level of evidence for an increased movement time and for an increased number of errors during head aiming tasks was found. Moderate evidence was found in traumatic neck pain for a decreased mean velocity, peak acceleration, and reaction time, and for point deviation and time on target during head aiming tasks. Moderate evidence was found for decreased acceleration during unconstrained movements, too. Results on the remaining movement task and outcome variables showed only limited, very limited or even conflicting level of evidence for patients with neck pain to differ from controls.

CONCLUSIONS

Sensorimotor control in NP in the way of kinematic and movement accuracy characteristics of head motion was examined in head aiming, functional or unconstrained movement tasks. The results from this review indicate that for some characteristics that describe sensorimotor control, patients with NP differ from healthy controls.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration number: CRD42020139083.

Sensorimotor performance in acute-subacute non-specific neck pain: a non-randomized prospective clinical trial with intervention

Background

The assessment of cervical spine kinematic axial rotation performance is of great importance in the context of the study of neck sensorimotor control. However, studies addressing the influence of the level of provocation of spinal pain and the potential benefit of passive manual therapy mobilizations in patients with acute-subacute non-specific neck pain are lacking.

Methods

A non-randomized prospective clinical trial with an intervention design was conducted. We investigated: (1) the test-retest reliability of kinematic variables during a fast axial head rotation task standardized with the DidRen laser test device in 42 Healthy pain-free Control Participants (HCP) (24.3 years ±6.8); (2) the differences in kinematic variables between HCP and 38 patients with Acute-subacute Non-Specific neck Pain (ANSP) assigned to two different groups according to whether their pain was localized in the upper or lower spine (46.2 years ±16.3); and (3) the effect of passive manual therapy mobilizations on kinematic variables of the neck during fast axial head rotation.

Results

(1) Intra-class correlation coefficients ranged from moderate (0.57 (0.06-0.80)) to excellent (0.96 (0.91-0.98)). (2) Kinematic performance during fast axial rotations of the head was significantly altered in ANSP compared to HCP (age-adjusted) for one variable: the time between peaks of acceleration and deceleration (p<0.019). No significant difference was observed between ANSP with upper vs lower spinal pain localization. (3) After the intervention, there was a significant effect on several kinematic variables, e.g., ANSP improved peak speed (p<0.007) and performance of the DidRen laser test (p<0.001), with effect sizes ranging from small to medium.

Conclusion

(1) The DidRen laser test is reliable. (2) A significant reduction in time between acceleration and deceleration peaks was observed in ANSP compared to HCP, but with no significant effect of spinal pain location on kinematic variables was found. (3) We found that neck pain decreased after passive manual therapy mobilizations with improvements of several kinematic variables.

Trial registration

Registration Number: NCT 04407637

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-021-04876-4.

The Validity and Reliability of the Microsoft Kinect for Measuring Trunk Compensation during Reaching

Compensatory movements at the trunk are commonly utilized during reaching by persons with motor impairments due to neurological injury such as stroke. Recent low-cost motion sensors may be able to measure trunk compensation, but their validity and reliability for this application are unknown. The purpose of this study was to compare the first (K1) and second (K2) generations of the Microsoft Kinect to a video motion capture system (VMC) for measuring trunk compensation during reaching. Healthy participants (n = 5) performed reaching movements designed to simulate trunk compensation in three different directions and on two different days while being measured by all three sensors simultaneously. Kinematic variables related to reaching range of motion (ROM), planar reach distance, trunk flexion and lateral flexion, shoulder flexion and lateral flexion, and elbow flexion were calculated. Validity and reliability were analyzed using repeated-measures ANOVA, paired t-tests, Pearson’s correlations, and Bland-Altman limits of agreement. Results show that the K2 was closer in magnitude to the VMC, more valid, and more reliable for measuring trunk flexion and lateral flexion during extended reaches than the K1. Both sensors were highly valid and reliable for reaching ROM, planar reach distance, and elbow flexion for all conditions. Results for shoulder flexion and abduction were mixed. The K2 was more valid and reliable for measuring trunk compensation during reaching and therefore might be prioritized for future development applications. Future analyses should include a more heterogeneous clinical population such as persons with chronic hemiparetic stroke.

Timed Up and Go and Six-Minute Walking Tests with Wearable Inertial Sensor: One Step Further for the Prediction of the Risk of Fall in Elderly Nursing Home People

Assessing the risk of fall in elderly people is a difficult challenge for clinicians. Since falls represent one of the first causes of death in such people, numerous clinical tests have been created and validated over the past 30 years to ascertain the risk of falls. More recently, the developments of low-cost motion capture sensors have facilitated observations of gait differences between fallers and nonfallers. The aim of this study is twofold. First, to design a method combining clinical tests and motion capture sensors in order to optimize the prediction of the risk of fall. Second to assess the ability of artificial intelligence to predict risk of fall from sensor raw data only. Seventy-three nursing home residents over the age of 65 underwent the Timed Up and Go (TUG) and six-minute walking tests equipped with a home-designed wearable Inertial Measurement Unit during two sets of measurements at a six-month interval. Observed falls during that interval enabled us to divide residents into two categories: fallers and nonfallers. We show that the TUG test results coupled to gait variability indicators, measured during a six-minute walking test, improve (from 68% to 76%) the accuracy of risk of fall’s prediction at six months. In addition, we show that an artificial intelligence algorithm trained on the sensor raw data of 57 participants reveals an accuracy of 75% on the remaining 16 participants.