Feature extraction and quantification of the variability of dynamic performance profiles due to the different sagittal lift characteristics

Dynamic assessment for low back-support exoskeletons during manual handling tasks

Exoskeletons can protect users' lumbar spine and reduce the risk of low back injury during manual lifting tasks. Although many exoskeletons have been developed, their adoptability is limited by their task- and movement-specific effects on reducing burden. Many studies have evaluated the safety and effectiveness of an exoskeleton using the peak/mean values of biomechanical variables, whereas the performance of the exoskeleton at other time points of the movement has not been investigated in detail. A functional analysis, which presents discrete time-series data as continuous functions, makes it possible to highlight the features of the movement waveform and determine the difference in each variable at each time point. This study investigated an assessment method for exoskeletons based on functional ANOVA, which made it possible to quantify the differences in the biomechanical variables throughout the movement when using an exoskeleton. Additionally, we developed a method based on the interpolation technique to estimate the assistive torque of an exoskeleton. Ten men lifted a 10-kg box under symmetric and asymmetric conditions five times each. Lumbar load was significantly reduced during all phases (flexion, lifting, and laying) under both conditions. Additionally, reductions in kinematic variables were observed, indicating the exoskeleton's impact on motion restrictions. Moreover, the overlap F-ratio curves of the lumbar load and kinematic variables imply that exoskeletons reduce the lumbar load by restricting the kinematic variables. The results suggested that at smaller trunk angles (<25°), an exoskeleton neither significantly reduces the lumbar load nor restricts trunk movement. Our findings will help increasing exoskeleton safety and designing effective products for reducing lumbar injury risks.

The effects of movement speed on kinematic variability and dynamic stability of the trunk in healthy individuals and low back pain patients

BACKGROUND

Comparison of the kinematic variability and dynamic stability of the trunk between healthy and low back pain patient groups can contribute to gaining valuable information about the movement patterns and neuromotor strategies involved in various movement tasks.

METHODS

Fourteen chronic low back pain patients with mild symptoms and twelve healthy male volunteers performed repeated trunk flexion-extension movements in the sagittal plane at three different speeds: 20 cycles/min, self-selected, and 40 cycles/min. Mean standard deviations, coefficient of variation and variance ratio as variability measures; maximum finite-time Lyapunov exponents and maximum Floquet multipliers as stability measures were computed from trunk kinematics.

FINDINGS

Higher speed significantly reduced the kinematic variability, while it increased short-term Lyapunov exponents. Long-term Lyapunov exponents were higher at self-selected speed and lower in low back pain patients as compared to control volunteers. Floquet multipliers were larger at self-selected speed and during higher pace trunk movements.

INTERPRETATION

Our findings suggest that slower pace flexion-extension trunk movements are associated with more motor variation as well as local and orbital stability, implying less potential risk of injury for the trunk. Individuals with and without low back pain consistently recruited a closed-loop control strategy towards achieving trunk stability. Chronic low back pain patients exhibited more stable trunk movements over long-term periods, indicating probable temporary pain relief functional adaption strategies. These results may be used towards the development of more effective personalized rehabilitation strategies and quantitative spinal analysis tools for low back pain detection, diagnosis and treatment, as well as improvement of workspace and occupational settings.

DEVELOPMENT OF A GENETIC ALGORITHM BASED BIOMECHANICAL SIMULATION OF SAGITTAL LIFTING TASKS

Fibrin sealant and platelet gels are human blood-derived, biodegradable, non toxic, surgical products obtained by mixing a fibrinogen concentrate or a platelet rich plasma with thrombin, respectively. Fibrin sealant is now a well known surgical tool increasingly used to stop or control bleeding, or to provide air and fluid tightness in many surgical situations. Platelet gels are newly developed preparations that are of specific interest because they contain numerous physiological growth factors and cytikines that are released upon the activation of blood platelets by thrombin. These growth factors, including PDGF, TGF- β1, BMP, and VEGF have been shown to stimulate cell growth and differentiation with special clinical benefits for soft and bony tissue healing and regeneration. Platelet gels allow surgeons to manipulate the cellular environment of surgical sites and to guide tissue regeneration. A specific interest of such products is observed for the induction of osteogenesis and chondrogenesis. Advances in the preparation, clinical use, and safety of these two important classes of blood-derived biomaterials are reviewed.

Posture and motion variability in non-repetitive manual materials handling tasks

In developing a motion prediction model it is important to initially consider the sources of variability that a model should reproduce. This initial step is followed by model evaluation, where the variability predicted by the model can be a useful test parameter. An existing lifting-motion dataset collected under controlled laboratory conditions was employed here to evaluate quantitatively some important sources of variability for lift motion modeling. The main source of variability was the segment being analyzed, which accounted for more than 20% of the overall variability. There was substantial left-right symmetry in individual segment variability estimates, which were largest for the upper arm segment and tended to be larger for the upper limbs than the lower limbs. Task-related factors accounted for variability mainly as a function of the segment being considered. Within-participant variability contributions to the dataset were relatively small, whereas the contribution of between-participants variability was dependent on the segment (as large as 50%) and could indicate different lifting strategies across participants. Variability was found to remain relatively constant across the different stages of the lifting movements. Implications of these results for the development and evaluation of motion prediction models are presented. Specifically, while task characteristics may be important modifiers of the mean segment trajectory during a lifting movement, their influence on variability differs based on the segment that is being considered. The relevance of the findings is discussed in terms of their utility in the ergonomic design of tasks and work spaces.

Differentiating lifting technique between those who develop low back pain and those who do not

BACKGROUND

No research to date has been able to discriminate differences in lifting technique for healthy individuals who eventually develop low back pain compared to those that do not while employed in a manual materials handling industry. The purpose of this study was to demonstrate the ability of principal component analysis to identify differences in lifting technique.

METHODS

Principal component analysis was applied to sixteen kinematic and kinetic waveforms describing the two-dimensional motion of the trunk and load. The principal component scores for each variable were used as the dependent measures in a one-way ANOVA to determine group differences.

FINDINGS

Significant group differences (P<0.05) were found for five of the principal component scores capturing associated kinematic waveform patterns related to the control and placement of the box on the shelf, and associated kinetic waveform patterns related to the relative timing of extension moment generation in the sacral and thoracic regions. A related waveform pattern for trunk compression was also found.

INTERPRETATION

Due to the coordinated movements involved in tasks such as lifting, differences among clinical populations have been difficult to demonstrate empirically. We were able to identify different characteristics in lifting kinematics and kinetics prior to the development of low back pain. Principal component analysis was able to identify important biomechanical differences where traditional analyses failed. This is the first study to identify such lifting differences prior to the development of low back pain.

Principal components analysis as an evaluation and classification tool for lower torso sEMG data

The use of univariate statistical techniques on multivariate electromyography data can fail to uncover important relationships between variables. Principal components analysis (PCA) is a multivariate statistical technique that can be used as a data exploration tool, both by classifying participants and simplifying data structures. Past research using this technique has focused on discriminating between "patients" and "normals". This investigation explored the use of PCA on electromyography data from healthy participants, with the objective of elucidating any between-participant differences in the multivariate patterns of muscle coactivation. Results indicated that, even between healthy participants, quantitative and qualitative differences in muscle coactivation patterns exist and that, in the context of the lower torso, a large portion (>70%) of the empirically determined muscle activation could be synthesized in a theoretical three-parameter control model.