Body center of mass trajectory and mechanical energy using inertial sensors: a feasible stride?

Walking symmetry is speed and index dependent

Gait symmetry is one of the most informative aspects describing the quality of gait. Many indices have been proposed to quantify gait symmetry. Among them, indices focusing on the comparison of the two body sides (e.g., Symmetry Angle, SA) and indices based on the analysis of the locomotor act as a whole, dealing with the body center of mass (e.g., Symmetry Index, SIBCoM) or lower trunk accelerometry (e.g., improved Harmonic Ratio, iHR) have been proposed. Remarkably, the relationship between these indices has received little attention so far, as well as the influence of gait speed on their values. The aim of this study is to investigate this relationship by comparing the SA, SIBCoM, and iHR, and to explore the effect of walking speed on these indices. Ten healthy adults walked for 60 s on a treadmill at seven different speeds (from 0.28 to 1.95 m s-1) and simulate an asymmetric gait (ASYM) at 0.83 m s-1. Marker-based trajectories were recorded, and the body center of mass 3D trajectory was obtained. Simultaneously, lower trunk 3D linear accelerations were collected using a triaxial accelerometer. SIBCoM, iHR, and SA were calculated for each stride, each anatomical direction, and each condition. Perfect symmetry was never displayed in any axes and any indices. Significant differences existed between SIBCoM, and iHR in all anatomical directions (p < 0.0001). The walking speed significantly affected SIBCoM and iHR values in anteroposterior and craniocaudal directions, but not in mediolateral. Conversely, no walking speed effect was found for SA (p = 0.28). All three indices significantly discriminated between ASYM and the corresponding walking condition (p < 0.05). Gait symmetry may differ significantly according to the data source, mathematical approach, and walking speed. Healthy individuals display an asymmetrical gait and acknowledging this aspect is crucial when establishing rehabilitation objectives and assessing the quality of gait in the clinical setting.

Fatigue Affects Body Acceleration During Vertical Jumping and Agility Tasks in Elite Young Badminton Players

BACKGROUND

Badminton is a sport demanding both high aerobic and anaerobic fitness levels, and fatigue can significantly impact game performance. However, relevant studies are limited, and none have employed a wearable inertial measurement unit (IMU) to investigate the effects of fatigue on athletic performance in the field.

HYPOTHESIS

Overall performance and body acceleration in both time and frequency domains during the fundamental badminton skills of vertical jumping and changes of direction will be affected by fatigue.

STUDY DESIGN

Cross-sectional study.

LEVEL OF EVIDENCE

Level 3.

METHODS

A total of 38 young badminton players competing at the Division I level participated. Body accelerations while performing vertical jump and agility-T tests before and immediately after undergoing a fatigue protocol were measured by an IMU, positioned at the L4 to L5 level.

RESULTS

Jumping height decreased significantly by 4 cm (P < 0.01) after fatigue with greater downward acceleration (1.03 m/s2, P < 0.05) during the squatting subphase. Finishing time increased significantly by 50 ms only during the 10-m side-shuffling of the agility-T test (P = 0.02) after fatigue with greater peak and mean accelerations (3.83 m/s2, P = 0.04; 0.43 m/s2, P < 0.01), and higher median and mean frequency (0.38 Hz, P = 0.04, 0.11 Hz, P = 0.01).

CONCLUSION

This study using a wearable IMU demonstrates the effects of fatigue on body acceleration in badminton players. The frequency-domain analysis further indicated that fatigue might lead to loss of voluntary control of active muscles and increased impacts on the passive elastic elements.

CLINICAL RELEVANCE

The findings imply that fatigue can lead to diminished athletic performance and highlight the potential for an increased risk of sports injuries. Consequently, maintaining precision in monitoring fatigue is crucial for elite young badminton players.

The effect of model complexity on the human center of mass estimation using the statically equivalent serial chain technique

Estimating the human center of mass (CoM) has long been recognized as a highly complex process. A relatively recent and noteworthy technique for CoM estimation that has gained popularity is the statically equivalent serial chain (SESC). This technique employs a remodeling of the human skeleton as a serial chain where the end effector represents the CoM location. In this study, we aimed to evaluate the impact of model complexity on the estimation capability of the SESC technique. To achieve this, we designed and rigorously assessed four distinct models with varying complexities against the static center of pressure (CoP) as reference, by quantifying both the root-mean-square (RMS) and correlation metrics. In addition, the Bland-Altman analysis was utilized to quantify the agreement between the estimations and reference values. The findings revealed that increasing the model complexity significantly improved CoM estimation quality up to a specific threshold. The maximum observed RMS difference among the models reached 9.85 mm. However, the application and task context should be considered, as less complex models still provided satisfactory estimation performance. In conclusion, the evaluation of model complexity demonstrated its impact on CoM estimation using the SESC technique, providing insights into the trade-off between accuracy and complexity in practical applications.

Motion Capture Technologies for Ergonomics: A Systematic Literature Review

Muscular skeletal disorder is a difficult challenge faced by the working population. Motion capture (MoCap) is used for recording the movement of people for clinical, ergonomic and rehabilitation solutions. However, knowledge barriers about these MoCap systems have made them difficult to use for many people. Despite this, no state-of-the-art literature review on MoCap systems for human clinical, rehabilitation and ergonomic analysis has been conducted. A medical diagnosis using AI applies machine learning algorithms and motion capture technologies to analyze patient data, enhancing diagnostic accuracy, enabling early disease detection and facilitating personalized treatment plans. It revolutionizes healthcare by harnessing the power of data-driven insights for improved patient outcomes and efficient clinical decision-making. The current review aimed to investigate: (i) the most used MoCap systems for clinical use, ergonomics and rehabilitation, (ii) their application and (iii) the target population. We used preferred reporting items for systematic reviews and meta-analysis guidelines for the review. Google Scholar, PubMed, Scopus and Web of Science were used to search for relevant published articles. The articles obtained were scrutinized by reading the abstracts and titles to determine their inclusion eligibility. Accordingly, articles with insufficient or irrelevant information were excluded from the screening. The search included studies published between 2013 and 2023 (including additional criteria). A total of 40 articles were eligible for review. The selected articles were further categorized in terms of the types of MoCap used, their application and the domain of the experiments. This review will serve as a guide for researchers and organizational management.

Center of mass-based posturography for free living environment applications

BACKGROUND

Postural assessment is crucial as risk of falling is a major health problem for the elderly. The most widely used devices are force and balance plates, while center of pressure is the most studied parameter as measure of neuromuscular imbalances of the body sway. In out-of-laboratory conditions, where the use of plates is unattainable, the center of mass can serve as an alternative. This work proposes a center of mass-based posturographic measurement for free living applications.

METHODS

Ten healthy and ten Parkinson's disease individuals (age = 26.1 ± 1.5, 70.4 ± 6.2 years, body mass index = 21.7 ± 2.2, 27.6 ± 2.8 kg/m², respectively) participated in the study. A stereophotogrammetric system and a force plate were used to acquire the center of pressure and the 5th lumbar vertebra displacements during the Romberg test. The center of mass was estimated using anthropometric measures. Posturographic parameters were extracted from center of pressure, center of mass and 5th lumbar vertebra trajectories. Normalized root mean squared difference was used as metric to compare the trajectories; Spearman's correlation coefficient was computed among the posturographic parameters.

FINDINGS

Low values of the metric indicated a good agreement between 5th lumbar vertebra trajectory and both center of pressure and center of mass trajectories. Statistically significant correlations were found among the postural variables.

INTERPRETATION

A method to perform posturography tracking the movement of the 5th lumbar vertebra as an approximation of center of mass has been presented and validated. The method requires the solely kinematic tracking of one anatomical landmark with no need of plates for free living applications.

Center of mass velocity comparison using a whole body magnetic inertial measurement unit system and force platforms in well trained sprinters in straight-line and curve sprinting

BACKGROUND

Sprint performance can be characterized through the centre of mass (COM) velocity over time. In-field computation of the COM is key in sprint training.

RESEARCH QUESTION

To compare the stance-averaged COM velocity computation from a Magneto-Inertial Measurement Units (MIMU) to a reference system: force platforms (FP), over the early acceleration phase in both straight and curve sprinting.

METHODS

Nineteen experienced-to-elite track sprinters performed 1 maximal sprint on both the straight and the curve (radius = 41.58 m) in a randomized order. Utilizing a MIMU-based system (Xsens MVN Link) and compared to FP (Kistler), COM velocity was computed with both systems. Averaged stance-by-stance COM velocity over straight-line and curve sprinting following the vertical axis (respectively V_zMIMU and V_zFP) and the norm of the two axes lying on the horizontal plane: x and y, approximately anteroposterior and mediolateral (respectively V_xyMIMU and V_xyFP) over the starting-blocks (SB) and initial acceleration (IA - composed out of the first four stances following the SB) were compared using mean bias, 95 % limits of agreements and Pearson's correlation coefficients.

RESULTS

148 stances were analyzed. V_xyMIMU mean bias was comprised between 0.26 % and 2.03 % (expressed in % with respect to the FP) for SB, 5.63 % and 7.29 % over IA respectively on the straight and the curve. Pearson's correlation coefficients ranged between 0.943 and 0.990 for V_xy, 0.423 and 0.938 for V_z. On the other hand, V_zMIMU mean bias ranged between 2.33 % and 4.69 % for SB, between 1.44 % and 19.95 % over IA respectively on the straight and the curve SIGNIFICANCE: The present findings suggest that the MIMU-based system tested slightly underestimated V_xyMIMU, though within narrow limits which supports its utilization. On the other hand, V_zMIMU computation in sprint running is not fully mature yet. Therefore, this MIMU-based system represents an interesting device for in-fieldV_xyMIMU computation either for straight-line and curve sprinting.

Evaluation of center of mass estimation for obese using statically equivalent serial chain

The complex structure of the human body makes its center of mass (CoM) estimation very challenging. The typically used estimation methods usually suffer from large estimation errors when applied to bodies with structural differences. Thus, a reliable estimation method is of utmost importance. In this paper, we present a detailed evaluation of a subject-specific CoM estimation technique named Statically Equivalent Serial Chain (SESC) by investigating its estimation ability over two different groups of subjects (Fit and Obese) in comparison to the segmental analysis method. For this study, we used an IMU-based motion capture system and a force platform to record the joint angles and corresponding center of pressure (CoP) values of twenty-five participants while performing a series of static postures. The root-mean-square errors (RMSE) of SESC's estimation for both groups showed close and lower mean values, whereas the segmental analysis method showed significantly larger RMSE values in comparison to SESC (p < 0.05). In addition, we used the Bland-Altman analysis to evaluate the agreement between the two techniques and the ground truth CoP, which showed the accuracy, precision, and reliability of SESC over both groups. In contrast, the segmental analysis method did not present neither accurate nor precise estimations, as our analysis revealed considerable fixed and proportional biases.

A New Quantitative Gait Analysis Method Based on Oscillatory Mechanical Energies Measured near Body Center of Mass

Human locomotion involves the modulation of whole-body mechanical energy, which can be approximated by the motion dynamics at the body’s center of mass (BCOM). This study introduces a new method to measure gait efficiency based on BCOM oscillatory kinetic energy patterns using a single inertia measurement unit (IMU). Forty-seven participants completed an overground walk test at a self-selected speed. The average oscillatory energy (OE) at BCOM during walking was derived from measured acceleration data. The total OE showed a positive correlation with forward-walking velocity. The ratio of total OE to constant forward kinetic energy for healthy adults varied from ~1−5%, which can be considered the percent of oscillatory energy required to maintain gait posture for a given forward-walking velocity. Mathematically, this ratio is proportional to the square of the periodic peak-to-peak displacement of BCOM. Individuals with gait impairments exhibited a higher percentage of oscillatory energy, typically >6%. This wearable IMU-based method has the potential to be an effective tool for the rapid, quantitative assessment of gait efficiency in clinical and rehabilitation settings.

A Center of Mass Estimation and Control Strategy for Body-Weight-Support Treadmill Training

Walking disorders are common in post-stroke. Body weight support (BWS) systems have been proposed and proven to enhance gait training systems for recovering in individuals with hemiplegia. However, the fixed weight support and walking speed increase the risk of falling and decrease the active participation of the subjects. This paper proposes a strategy to enhance the efficiency of BWS treadmill training. It consists in regulating the height of the BWS system to track the height of the subject's center of mass (CoM), whereby the CoM is estimated through a long-short term memory (LSTM) network and a locomotion recognition system. The LSTM network takes the walking speed, body-height to leg-length ratio, hip and knee joint angles of the hemiplegic subjects' non-paretic side from the locomotion recognition system as input signals and outputs the CoM height to a BWS treadmill training robot. Besides, the hip and knee joints' ranges of motion are increased by 34.54% and 25.64% under the CoM height regulation compared to the constant weight support, respectively. With the CoM height regulation strategy, the stance phase duration of the paretic side is significantly increased by 14.6% of the gait cycle, and the symmetry of the gait is also promoted. The CoM height kinematics by adjustment strategy is in good agreement with the mean values of the 14 non-disabled subjects, which demonstrated that the adjustment strategy improves the stability of CoM height during the training.

Three-dimensional acceleration of the body center of mass in people with transfemoral amputation: Identification of a minimal body segment network

BACKGROUND

The analysis of biomechanical parameters derived from the body center of mass (BCoM) 3D motion allows for the characterization of gait impairments in people with lower-limb amputation, assisting in their rehabilitation. In this context, magneto-inertial measurement units are promising as they allow to measure the motion of body segments, and therefore potentially of the BCoM, directly in the field. Finding a compromise between the accuracy of computed parameters and the number of required sensors is paramount to transfer this technology in clinical routine.

RESEARCH QUESTION

Is there a reduced subset of instrumented segments (BSN) allowing a reliable and accurate estimation of the 3D BCoM acceleration transfemoral amputees?

METHODS

The contribution of each body segment to the BCoM acceleration was quantified in terms of weight and similarity in ten people with transfemoral amputation. First, body segments and BCoM accelerations were obtained using an optoelectronic system and a full-body inertial model. Based on these findings, different scenarios were explored where the use of one sensor at pelvis/trunk level and of different networks of segment-mounted sensors for the BCoM acceleration estimation was simulated and assessed against force plate-based reference acceleration.

RESULTS

Trunk, pelvis and lower-limb segments are the main contributors to the BCoM acceleration in transfemoral amputees. The trunk and shanks BSN allows for an accurate estimation of the sagittal BCoM acceleration (Normalized RMSE ≤ 13.1 %, Pearson's correlations r ≥ 0.86), while five segments are necessary when the 3D BCoM acceleration is targeted (Normalized RMSE ≤ 13.2 %, Pearson's correlations r ≥ 0.91).

SIGNIFICANCE

A network of three-to-five segments (trunk and lower limbs) allows for an accurate estimation of 2D and 3D BCoM accelerations. The use of a single pelvis- or trunk-mounted sensor does not seem advisable. Future studies should be performed to confirm these results where inertial sensor measured accelerations are considered.

Deep neural network approach for estimating the three-dimensional human center of mass using joint angles

Human body center of mass location plays an essential role in physical therapy, especially in investigating a subject's capability to maintain balance. However, its estimation can be a very complex, costly, and time-consuming process. To overcome the complexities and reduce the hardware cost, we proposed a deep neural network model to map the measurements of body joint angles to the 3-D center of mass position. We used an inertial measurement units-based motion-capture system (Xsens MVN Awinda) to record the joint angles and center of mass positions of 22 healthy subjects. We divided the subjects into two groups and assigned them either squat or gait tasks. Then, recorded data were merged and fed to the model to increase its generalizability. We evaluated five different input combinations to assess the effect of each input on the accuracy and generalizability of the model. The accuracy and generalizability of the models were evaluated by root-mean-square errors and comparing the differences in errors for different datasets, respectively. Root-mean-square errors ranged from 4.11 mm to 18.39 mm on both training and testing datasets for different models. Besides, adding anthropometric measurements and a Boolean parameter specifying the type of motion contributed significantly to the generalizability of the model. Also, adding unnecessary joint angles had adverse effects on the network's estimations. This study showed that by using deep neural networks, the center of mass estimations could be achieved with high accuracy, and a 17 sensors motion-capture system can be replaced with only five sensors, thus reducing the cost and complexity of the process.

Estimation of Human Center of Mass Position through the Inertial Sensors-Based Methods in Postural Tasks: An Accuracy Evaluation

The estimation of the body’s center of mass (CoM) trajectory is typically obtained using force platforms, or optoelectronic systems (OS), bounding the assessment inside a laboratory setting. The use of magneto-inertial measurement units (MIMUs) allows for more ecological evaluations, and previous studies proposed methods based on either a single sensor or a sensors’ network. In this study, we compared the accuracy of two methods based on MIMUs. Body CoM was estimated during six postural tasks performed by 15 healthy subjects, using data collected by a single sensor on the pelvis (Strapdown Integration Method, SDI), and seven sensors on the pelvis and lower limbs (Biomechanical Model, BM). The accuracy of the two methods was compared in terms of RMSE and estimation of posturographic parameters, using an OS as reference. The RMSE of the SDI was lower in tasks with little or no oscillations, while the BM outperformed in tasks with greater CoM displacement. Moreover, higher correlation coefficients were obtained between the posturographic parameters obtained with the BM and the OS. Our findings showed that the estimation of CoM displacement based on MIMU was reasonably accurate, and the use of the inertial sensors network methods should be preferred to estimate the kinematic parameters.