Frederik Petri Svenningsen

Bio: Frederik Petri Svenningsen is an academic researcher from Aalborg University. The author has contributed to research in topics: Gait (human) & STRIDE. The author has an hindex of 3, co-authored 4 publications receiving 21 citations.

Estimation of Spinal Loading During Manual Materials Handling Using Inertial Motion Capture

Abstract: Musculoskeletal models have traditionally relied on measurements of segment kinematics and ground reaction forces and moments (GRF&Ms) from marked-based motion capture and floor-mounted force plates, which are typically limited to laboratory settings. Recent advances in inertial motion capture (IMC) as well as methods for predicting GRF&Ms have enabled the acquisition of these input data in the field. Therefore, this study evaluated the concurrent validity of a novel methodology for estimating the dynamic loading of the lumbar spine during manual materials handling based on a musculoskeletal model driven exclusively using IMC data and predicted GRF&Ms. Trunk kinematics, GRF&Ms, L4-L5 joint reaction forces (JRFs) and erector spinae muscle forces from 13 subjects performing various lifting and transferring tasks were compared to a model driven by simultaneously recorded skin-marker trajectories and force plate data. Moderate to excellent correlations and relatively low magnitude differences were found for the L4-L5 axial compression, erector spinae muscle and vertical ground reaction forces during symmetrical and asymmetrical lifting, but discrepancies were also identified between the models, particularly for the trunk kinematics and L4-L5 shear forces. Based on these results, the presented methodology can be applied for estimating the relative L4-L5 axial compression forces under dynamic conditions during manual materials handling in the field.

A narrative review of potential measures of dynamic stability to be used during outdoor locomotion on different surfaces

Abstract: Dynamic stability of locomotion plays an important role in running injuries, particularly during trail running where ankle injuries occur frequently. Several studies have investigated dynamic stability of locomotion using wearable accelerometer measurements. However, no study has reviewed how dynamic stability of locomotion is quantified using accelerometry. Therefore, the present review aims to synthetise the methods and findings of studies investigating stability related parameters measured by accelerometry, during locomotion on various surfaces, and among asymptomatic participants. A systematic search of studies associated with locomotion was conducted. Only studies including assessment of dynamic stability parameters based on accelerometry, including at least one group of asymptomatic participants, and conditions that occur during trail running were considered relevant for this review. Consequently, all retrieved studies used a non-obstructive portable accelerometer or an inertial measurement unit. Fifteen studies used a single tri-axial accelerometer placed above the lumbar region allowing outdoor recordings. From trunk accelerations, a combination of index of cycle repeatability and signal dispersion can adequately be used to assess dynamic stability. However, as most studies included indoor conditions, studies addressing the biomechanics of trail running in outdoor conditions are warranted.

Biomechanics And Motor Control Of Human Movement

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Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture

Abstract: The present study deals with human awareness, which is a very important aspect of human–robot interaction. This feature is particularly essential in agricultural environments, owing to the information-rich setup that they provide. The objective of this investigation was to recognize human activities associated with an envisioned synergistic task. In order to attain this goal, a data collection field experiment was designed that derived data from twenty healthy participants using five wearable sensors (embedded with tri-axial accelerometers, gyroscopes, and magnetometers) attached to them. The above task involved several sub-activities, which were carried out by agricultural workers in real field conditions, concerning load lifting and carrying. Subsequently, the obtained signals from on-body sensors were processed for noise-removal purposes and fed into a Long Short-Term Memory neural network, which is widely used in deep learning for feature recognition in time-dependent data sequences. The proposed methodology demonstrated considerable efficacy in predicting the defined sub-activities with an average accuracy of 85.6%. Moreover, the trained model properly classified the defined sub-activities in a range of 74.1–90.4% for precision and 71.0–96.9% for recall. It can be inferred that the combination of all sensors can achieve the highest accuracy in human activity recognition, as concluded from a comparative analysis for each sensor’s impact on the model’s performance. These results confirm the applicability of the proposed methodology for human awareness purposes in agricultural environments, while the dataset was made publicly available for future research.

Pattern control of human locomotion

Abstract: Abstract There is much experimental evidence for the existence of biomechanical constraints which simplify the problem of control of multi‐segment movements. In addition, it has been hypothesized that movements are controlled using a small set of basic temporal components or activation patterns, shared by several different muscles and reflecting global kinematic and kinetic goals. Here we review recent studies on human locomotion showing that muscle activity is accounted for by a combination of few basic patterns, each one timed at a different phase of the gait cycle. Similar patterns are involved in walking and running at different speeds, walking forwards or backwards, and walking under different loading conditions. The corresponding weights of distribution to different muscles may change as a function of the condition, allowing highly flexible control. Biomechanical correlates of each activation pattern have been described, leading to the hypothesis that the co‐ordination of limb and body segments arises from the coupling of neural oscillators between each other and with limb mechanical oscillators. Muscle activations need only intervene during limited time epochs to force intrinsic oscillations of the system when energy is lost.

Recommendations for determining the validity of consumer wearable and smartphone step count: expert statement and checklist of the INTERLIVE network.

Abstract: Consumer wearable and smartphone devices provide an accessible means to objectively measure physical activity (PA) through step counts. With the increasing proliferation of this technology, consumers, practitioners and researchers are interested in leveraging these devices as a means to track and facilitate PA behavioural change. However, while the acceptance of these devices is increasing, the validity of many consumer devices have not been rigorously and transparently evaluated. The Towards Intelligent Health and Well-Being Network of Physical Activity Assessment (INTERLIVE) is a joint European initiative of six universities and one industrial partner. The consortium was founded in 2019 and strives to develop best-practice recommendations for evaluating the validity of consumer wearables and smartphones. This expert statement presents a best-practice consumer wearable and smartphone step counter validation protocol. A two-step process was used to aggregate data and form a scientific foundation for the development of an optimal and feasible validation protocol: (1) a systematic literature review and (2) additional searches of the wider literature pertaining to factors that may introduce bias during the validation of these devices. The systematic literature review process identified 2897 potential articles, with 85 articles deemed eligible for the final dataset. From the synthesised data, we identified a set of six key domains to be considered during design and reporting of validation studies: target population, criterion measure, index measure, validation conditions, data processing and statistical analysis. Based on these six domains, a set of key variables of interest were identified and a ‘basic’ and ‘advanced’ multistage protocol for the validation of consumer wearable and smartphone step counters was developed. The INTERLIVE consortium recommends that the proposed protocol is used when considering the validation of any consumer wearable or smartphone step counter. Checklists have been provided to guide validation protocol development and reporting. The network also provide guidance for future research activities, highlighting the imminent need for the development of feasible alternative ‘gold-standard’ criterion measures for free-living validation. Adherence to these validation and reporting standards will help ensure methodological and reporting consistency, facilitating comparison between consumer devices. Ultimately, this will ensure that as these devices are integrated into standard medical care, consumers, practitioners, industry and researchers can use this technology safely and to its full potential.

The National Institute for Occupational Safety and Health (NIOSH) Recommended Weight Generates Different Spine Loads in Load-Handling Activity Performed Using Stoop, Semi-squat and Full-Squat Techniques; a Full-Body Musculoskeletal Model Study.

Abstract: OBJECTIVE Adequacy of the Revised NIOSH Lifting Equation (RNLE) in maintaining lumbosacral (L5-S1) loads below their recommended action limits in stoop, full-squat, and semi-squat load-handling activities was investigated using a full-body musculoskeletal model. BACKGROUND The NIOSH committee did not consider the lifting technique adapted by workers when estimating the recommended weight limit (RWL). It is currently unknown whether the lifting technique adapted by workers would affect the competence of the RNLE in keeping spine loads below their recommended limits. METHOD A full-body subject-specific musculoskeletal model (Anybody Modeling System, AMS) driven by a 10-camera Vicon motion capture system (Vicon Motion Systems Inc., Oxford, UK) was used to simulate different static stoop, semi-squat, and full-squat load-handling activities of ten normal-weight volunteers (mean of ∼70 kg corresponding to the 15th percentile of adult American males) with the task-specific NIOSH RWL held in hands. RESULTS Two-way repeated measures ANOVA revealed a significant effect of lifting technique on both the L5-S1 compression (p = 0.003) and shear (p = 0.004) loads with semi-squat technique resulting in significantly larger loads than both stoop and full-squat techniques (p < 0.05). While mean of L5-S1 loads remained smaller than their recommended limits, it is much expected that they pass these limits for heavier individuals, that is, for the 50th percentile of adult American males. CONCLUSION Spinal loads are expected to pass their recommended limits for heavier individuals especially during semi-squat lifting as the most frequently adapted technique by workers. APPLICATION Caution is required for the assessment of semi-squat lifting activities by the RNLE.