Technological Solutions for Human Movement Analysis in Obese Subjects: A Systematic Review

Trunk kinematic analysis of ascent and descent stairs in college students with idiopathic scoliosis: a case-control study

BACKGROUND CONTEXT

Traditional 3D motion analysis typically considers the spine as a rigid entity. Nevertheless, previous single-joint models have proven inadequate in evaluating the movement across different spinal segments in patients with idiopathic scoliosis (IS). Scoliosis significantly impairs movement functions, especially during activities such as ascending and descending stairs. There is a lack of research on the patterns of stair movement specifically for patients with IS.

PURPOSE

This study aims to investigate trunk kinematics in college students with IS during stair ascent and descent tasks. A total of 56 participants, 28 with IS and 28 with healthy controls, were recruited for this case-control study. The trunk movements were analyzed using a motion analysis system that incorporated a multisegment spine model. Understanding the multi-segment spine kinematics during stair tasks can contribute to the development of effective rehabilitation programs for individuals with IS.

STUDY DESIGN

Case-control study.

SAMPLE SIZE

Twenty-eight IS and 28 controls.

OUTCOME MEASURES

Cobb angle, spinal curvature, spinal active range of motion (ROM), Kinematics.

METHODS

The Qualisys system (Gothenburg, Sweden) was utilized in this study with a sampling frequency of 150 Hz. It recorded the kinematics in the thoracic, lumbar, thoracic cage, and pelvis while ascending and descending stairs for both the 28 IS individuals and the 28 control participants. Additionally, clinical parameters such as the Cobb angle, curvature of the spine, spinal range of motion (ROM), and other relevant factors were concurrently assessed among the subjects. Project supported by the National Natural Science Foundation of China (Grant No. 82205306). The authors declare no conflict of interest in preparing this article.

RESULTS

The findings of this study revealed that IS individuals exhibited reduced kyphotic curvature in the sagittal plane (p<.05) when compared to the control group. In contrast, these IS patients displayed greater coronal curvature (Cobb angle) in the frontal plane and a more substantial difference in thoracic side bending range of motion in comparison to the control group (p.05). Moreover, during the ascending stair activity, IS patients showed reduced thoracic cage flexion-extension range of motion (p<.05), while displaying increased lumbar rotation range of motion and anterior-posterior pelvic tilt range of motion (p<.05) in contrast to the control group. Notably, the kinematic analysis during the descent of stairs indicated that IS patients exhibited a larger range of motion in thoracic flexion-extension, thoracic side bending, thoracic cage side bending, thoracic rotation, and thoracic cage rotation when compared to the control group (p<.05).

CONCLUSIONS

The results showed significant differences in trunk kinematics between the two groups during both stair ascent and descent tasks. The utilization of the "multisegment spine model" facilitates the acquisition of motion information across multiple segments of the spine in patients diagnosed with IS, effectively enhancing the assessment outcomes derived from imaging information. The three-dimensional structural deformity in the trunk affects both static and dynamic activity patterns. In different activity states, IS patients demonstrate stiff movements in certain segments while experiencing compensatory instability in others. In the future, clinical rehabilitation programs for IS should prioritize stair-related activity training.

Position paper on how technology for human motion analysis and relevant clinical applications have evolved over the past decades: Striking a balance between accuracy and convenience

BACKGROUND

Over the past decades, tremendous technological advances have emerged in human motion analysis (HMA).

RESEARCH QUESTION

How has technology for analysing human motion evolved over the past decades, and what clinical applications has it enabled?

METHODS

The literature on HMA has been extensively reviewed, focusing on three main approaches: Fully-Instrumented Gait Analysis (FGA), Wearable Sensor Analysis (WSA), and Deep-Learning Video Analysis (DVA), considering both technical and clinical aspects.

RESULTS

FGA techniques relying on data collected using stereophotogrammetric systems, force plates, and electromyographic sensors have been dramatically improved providing highly accurate estimates of the biomechanics of motion. WSA techniques have been developed with the advances in data collection at home and in community settings. DVA techniques have emerged through artificial intelligence, which has marked the last decade. Some authors have considered WSA and DVA techniques as alternatives to "traditional" HMA techniques. They have suggested that WSA and DVA techniques are destined to replace FGA.

SIGNIFICANCE

We argue that FGA, WSA, and DVA complement each other and hence should be accounted as "synergistic" in the context of modern HMA and its clinical applications. We point out that DVA techniques are especially attractive as screening techniques, WSA methods enable data collection in the home and community for extensive periods of time, and FGA does maintain superior accuracy and should be the preferred technique when a complete and highly accurate biomechanical data is required. Accordingly, we envision that future clinical applications of HMA would favour screening patients using DVA in the outpatient setting. If deemed clinically appropriate, then WSA would be used to collect data in the home and community to derive relevant information. If accurate kinetic data is needed, then patients should be referred to specialized centres where an FGA system is available, together with medical imaging and thorough clinical assessments.

Assessment of an IMU-Based Experimental Set-Up for Upper Limb Motion in Obese Subjects

In recent years, wearable systems based on inertial sensors opened new perspectives for functional motor assessment with respect to the gold standard motion capture systems. The aim of this study was to validate an experimental set-up based on 17 body-worn inertial sensors (Awinda, Xsens, The Netherlands), addressing specific body segments with respect to the state-of-the art system (VICON, Oxford Metrics Ltd., Oxford, UK) to assess upper limb kinematics in obese, with respect to healthy subjects. Twenty-three obese and thirty healthy weight individuals were simultaneously acquainted with the two systems across a set of three tasks for upper limbs (i.e., frontal arm rise, lateral arm rise, and reaching). Root Mean Square error (RMSE) was computed to quantify the differences between the measurements provided by the systems in terms of range of motion (ROM), whilst their agreement was assessed via Pearson's correlation coefficient (PCC) and Bland-Altman (BA) plots. In addition, the signal waveforms were compared via one-dimensional statistical parametrical mapping (SPM) based on a paired t-test and a two-way ANOVA was applied on ROMs. The overall results partially confirmed the correlation and the agreement between the two systems, reporting only a moderate correlation for shoulder principal rotation angle in each task (r~0.40) and for elbow/flexion extension in obese subjects (r = 0.66), whilst no correlation was found for most non-principal rotation angles (r < 0.40). Across the performed tasks, an average RMSE of 34° and 26° was reported in obese and healthy controls, respectively. At the current state, the presence of bias limits the applicability of the inertial-based system in clinics; further research is intended in this context.

Dual-Task Interference Effects on Lower-Extremity Muscle Activities during Gait Initiation and Steady-State Gait among Healthy Young Individuals, Measured Using Wireless Electromyography Sensors

To maintain a healthy lifestyle, adults rely on their ability to walk while simultaneously managing multiple tasks that challenge their coordination. This study investigates the impact of cognitive dual tasks on lower-limb muscle activities in 21 healthy young adults during both gait initiation and steady-state gait. We utilized wireless electromyography sensors to measure muscle activities, along with a 3D motion capture system and force plates to detect the phases of gait initiation and steady-state gait. The participants were asked to walk at their self-selected pace, and we compared single-task and dual-task conditions. We analyzed mean muscle activation and coactivation in the biceps femoris, vastus lateralis, gastrocnemius, and tibialis anterior muscles. The findings revealed that, during gait initiation with the dual-task condition, there was a decrease in mean muscle activation and an increase in mean muscle coactivation between the swing and stance limbs compared with the single-task condition. In steady-state gait, there was also a decrease in mean muscle activation in the dual-task condition compared with the single-task condition. When participants performed dual-task activities during gait initiation, early indicators of reduced balance capability were observed. Additionally, during dual-task steady-state gait, the knee stabilizer muscles exhibited signs of altered activation, contributing to balance instability.

A Scoping Review of the Validity and Reliability of Smartphone Accelerometers When Collecting Kinematic Gait Data

The aim of this scoping review is to evaluate and summarize the existing literature that considers the validity and/or reliability of smartphone accelerometer applications when compared to 'gold standard' kinematic data collection (for example, motion capture). An electronic keyword search was performed on three databases to identify appropriate research. This research was then examined for details of measures and methodology and general study characteristics to identify related themes. No restrictions were placed on the date of publication, type of smartphone, or participant demographics. In total, 21 papers were reviewed to synthesize themes and approaches used and to identify future research priorities. The validity and reliability of smartphone-based accelerometry data have been assessed against motion capture, pressure walkways, and IMUs as 'gold standard' technology and they have been found to be accurate and reliable. This suggests that smartphone accelerometers can provide a cheap and accurate alternative to gather kinematic data, which can be used in ecologically valid environments to potentially increase diversity in research participation. However, some studies suggest that body placement may affect the accuracy of the result, and that position data correlate better than actual acceleration values, which should be considered in any future implementation of smartphone technology. Future research comparing different capture frequencies and resulting noise, and different walking surfaces, would be useful.

3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks

Compared to magnetic resonance imaging (MRI) and X-ray computed tomography (CT), ultrasound imaging is safer, faster, and more widely applicable. However, the use of conventional ultrasound in transcranial brain imaging for adults is predominantly hindered by the high acoustic impedance contrast between the skull and soft tissue. This study introduces a 3D AI algorithm, Brain Imaging Full Convolution Network (BIFCN), combining waveform modeling and deep learning for precise brain ultrasound reconstruction. We constructed a network comprising one input layer, four convolution layers, and one pooling layer to train our algorithm. In the simulation experiment, the Pearson correlation coefficient between the reconstructed and true images was exceptionally high. In the laboratory, the results showed a slightly lower but still impressive coincidence degree for 3D reconstruction, with pure water serving as the initial model and no prior information required. The 3D network can be trained in 8 h, and 10 samples can be reconstructed in just 12.67 s. The proposed 3D BIFCN algorithm provides a highly accurate and efficient solution for mapping wavefield frequency domain data to 3D brain models, enabling fast and precise brain tissue imaging. Moreover, the frequency shift phenomenon of blood may become a hallmark of BIFCN learning, offering valuable quantitative information for whole-brain blood imaging.

Context Recognition Algorithms for Energy-Efficient Freezing-of-Gait Detection in Parkinson's Disease

Freezing of gait (FoG) is a disabling clinical phenomenon of Parkinson's disease (PD) characterized by the inability to move the feet forward despite the intention to walk. It is one of the most troublesome symptoms of PD, leading to an increased risk of falls and reduced quality of life. The combination of wearable inertial sensors and machine learning (ML) algorithms represents a feasible solution to monitor FoG in real-world scenarios. However, traditional FoG detection algorithms process all data indiscriminately without considering the context of the activity during which FoG occurs. This study aimed to develop a lightweight, context-aware algorithm that can activate FoG detection systems only under certain circumstances, thus reducing the computational burden. Several approaches were implemented, including ML and deep learning (DL) gait recognition methods, as well as a single-threshold method based on acceleration magnitude. To train and evaluate the context algorithms, data from a single inertial sensor were extracted using three different datasets encompassing a total of eighty-one PD patients. Sensitivity and specificity for gait recognition ranged from 0.95 to 0.96 and 0.80 to 0.93, respectively, with the one-dimensional convolutional neural network providing the best results. The threshold approach performed better than ML- and DL-based methods when evaluating the effect of context awareness on FoG detection performance. Overall, context algorithms allow for discarding more than 55% of non-FoG data and less than 4% of FoG episodes. The results indicate that a context classifier can reduce the computational burden of FoG detection algorithms without significantly affecting the FoG detection rate. Thus, implementation of context awareness can present an energy-efficient solution for long-term FoG monitoring in ambulatory and free-living settings.