Dataset Information

A human lower-limb biomechanics and wearable sensors dataset during cyclic and non-cyclic activities.

ABSTRACT: Tasks of daily living are often sporadic, highly variable, and asymmetric. Analyzing these real-world non-cyclic activities is integral for expanding the applicability of exoskeletons, protheses, wearable sensing, and activity classification to real life, and could provide new insights into human biomechanics. Yet, currently available biomechanics datasets focus on either highly consistent, continuous, and symmetric activities, such as walking and running, or only a single specific non-cyclic task. To capture a more holistic picture of lower limb movements in everyday life, we collected data from 12 participants performing 20 non-cyclic activities (e.g. sit-to-stand, jumping, squatting, lunging, cutting) as well as 11 cyclic activities (e.g. walking, running) while kinematics (motion capture and IMUs), kinetics (force plates), and electromyography (EMG) were collected. This dataset provides normative biomechanics for a highly diverse range of activities and common tasks from a consistent set of participants and sensors.

SUBMITTER: Scherpereel K

PROVIDER: S-EPMC10740031 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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A human lower-limb biomechanics and wearable sensors dataset during cyclic and non-cyclic activities.

Scherpereel Keaton K Molinaro Dean D Inan Omer O Shepherd Max M Young Aaron A

Scientific data 20231221 1

Tasks of daily living are often sporadic, highly variable, and asymmetric. Analyzing these real-world non-cyclic activities is integral for expanding the applicability of exoskeletons, protheses, wearable sensing, and activity classification to real life, and could provide new insights into human biomechanics. Yet, currently available biomechanics datasets focus on either highly consistent, continuous, and symmetric activities, such as walking and running, or only a single specific non-cyclic ta ...[more]

PMID: 38129422

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Project description:Abnormal lower limb biomechanics is speculated to be a risk factor for Achilles tendinopathy. This study systematically reviewed the existing literature to identify, critique and summarise lower limb biomechanical factors associated with Achilles tendinopathy.We searched electronic bibliographic databases (Medline, EMBASE, Current contents, CINAHL and SPORTDiscus) in November 2010. All prospective cohort and case-control studies that evaluated biomechanical factors (temporospatial parameters, lower limb kinematics, dynamic plantar pressures, kinetics [ground reaction forces and joint moments] and muscle activity) associated with mid-portion Achilles tendinopathy were included. Quality of included studies was evaluated using the Quality Index. The magnitude of differences (effect sizes) between cases and controls was calculated using Cohen's d (with 95% CIs).Nine studies were identified; two were prospective and the remaining seven case-control study designs. The quality of 9 identified studies was varied, with Quality Index scores ranging from 4 to 15 out of 17. All studies analysed running biomechanics. Cases displayed increased eversion range of motion of the rearfoot (d = 0.92 and 0.67 in two studies), reduced maximum lower leg abduction (d = -1.16), reduced ankle joint dorsiflexion velocity (d = -0.62) and reduced knee flexion during gait (d = -0.90). Cases also demonstrated a number of differences in dynamic plantar pressures (primarily the distribution of the centre of force), ground reaction forces (large effects for timing variables) and also showed reduced peak tibial external rotation moment (d = -1.29). Cases also displayed differences in the timing and amplitude of a number of lower limb muscles but many differences were equivocal.There are differences in lower limb biomechanics between those with and without Achilles tendinopathy that may have implications for the prevention and management of the condition. However, the findings need to be interpreted with caution due to the limited quality of a number of the included studies. Future well-designed prospective studies are required to confirm these findings.

Project description:Background: Studies aiming to objectively quantify movement disorders during upper limb tasks using wearable sensors have recently increased, but there is a wide variety in described measurement and analyzing methods, hampering standardization of methods in research and clinics. Therefore, the primary objective of this review was to provide an overview of sensor set-up and type, included tasks, sensor features and methods used to quantify movement disorders during upper limb tasks in multiple pathological populations. The secondary objective was to identify the most sensitive sensor features for the detection and quantification of movement disorders on the one hand and to describe the clinical application of the proposed methods on the other hand. Methods: A literature search using Scopus, Web of Science, and PubMed was performed. Articles needed to meet following criteria: 1) participants were adults/children with a neurological disease, 2) (at least) one sensor was placed on the upper limb for evaluation of movement disorders during upper limb tasks, 3) comparisons between: groups with/without movement disorders, sensor features before/after intervention, or sensor features with a clinical scale for assessment of the movement disorder. 4) Outcome measures included sensor features from acceleration/angular velocity signals. Results: A total of 101 articles were included, of which 56 researched Parkinson's Disease. Wrist(s), hand(s) and index finger(s) were the most popular sensor locations. Most frequent tasks were: finger tapping, wrist pro/supination, keeping the arms extended in front of the body and finger-to-nose. Most frequently calculated sensor features were mean, standard deviation, root-mean-square, ranges, skewness, kurtosis/entropy of acceleration and/or angular velocity, in combination with dominant frequencies/power of acceleration signals. Examples of clinical applications were automatization of a clinical scale or discrimination between a patient/control group or different patient groups. Conclusion: Current overview can support clinicians and researchers in selecting the most sensitive pathology-dependent sensor features and methodologies for detection and quantification of upper limb movement disorders and objective evaluations of treatment effects. Insights from Parkinson's Disease studies can accelerate the development of wearable sensors protocols in the remaining pathologies, provided that there is sufficient attention for the standardisation of protocols, tasks, feasibility and data analysis methods.

Dataset Information

A human lower-limb biomechanics and wearable sensors dataset during cyclic and non-cyclic activities.

Publications

A human lower-limb biomechanics and wearable sensors dataset during cyclic and non-cyclic activities.

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