Gait Partitioning Methods: A Systematic Review

Taborri, Juri; Palermo, Eduardo; Rossi, Stefano; Cappa, Paolo

doi:10.3390/s16010066

Cited by 287 publications

(209 citation statements)

References 90 publications

(157 reference statements)

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“…This would allow us to compare the LEC calculated using the optoelectronic system with that calculated by means of inertial sensors, thereby validating and strengthening the applicability of this method in indoor and outdoor work environments, and further supporting the findings of previous noteworthy studies in this field45, 46, 47 ) . An instrumental lifting recognition tool could be further implemented by using surface electromyography-based indices that would provide additional criteria of classification and enhance the power of the test.…”

Section: Discussionsupporting

confidence: 70%

Mechanical lifting energy consumption in work activities designed by means of the “revised NIOSH lifting equation”

et al. 2017

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The aims of the present work were: to calculate lifting energy consumption (LEC) in work activities designed to have a growing lifting index (LI) by means of revised NIOSH lifting equation; to evaluate the relationship between LEC and forces at the L5-S1 joint. The kinematic and kinetic data of 20 workers were recorded during the execution of lifting tasks in three conditions. We computed kinetic, potential and mechanical energy and the corresponding LEC by considering three different centers of mass of: 1) the load (CoML); 2) the multi-segment upper body model and load together (CoMUpp+L); 3) the whole body and load together (CoMTot). We also estimated compression and shear forces. Results shows that LEC calculated for CoMUpp+L and CoMTot grew significantly with the LI and that all the lifting condition pairs are discriminated. The correlation analysis highlighted a relationship between LEC and forces that determine injuries at the L5-S1 joint.

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Section: Discussionsupporting

confidence: 70%

Mechanical lifting energy consumption in work activities designed by means of the “revised NIOSH lifting equation”

et al. 2017

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“…Signals are now precisely understood and described, which allows for manual detection (Taborri et al, 2016). Nevertheless, this process can be long and painstaking and prevents any online analysis ( Figure 6 ).…”

Section: Resultsmentioning

confidence: 99%

Inertial Sensors to Assess Gait Quality in Patients with Neurological Disorders: A Systematic Review of Technical and Analytical Challenges

et al. 2017

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Gait disorders are major causes of falls in patients with neurological diseases. Understanding these disorders allows prevention and better insights into underlying diseases. InertiaLocoGraphy (ILG) –the quantification of gait by using inertial measurement units (IMUs) –shows great potential to address this public health challenge, but protocols vary widely and normative values of gait parameters are still unavailable. This systematic review critically compares ILG protocols, questions features extracted from inertial signals and proposes a semeiological analysis of clinimetric characteristics for use in neurological clinical routine. For this systematic review, PubMed, Cochrane and EMBASE were searched for articles assessing gait quality by using IMUs that were published from January 1, 2014 to August 31, 2016. ILG was used to assess gait in a wide range of neurological disorders – including Parkinson disease, mild cognitive impairment, Alzheimer disease, cerebral palsy, and cerebellar atrophy – as well as in the faller or frail older population and in people presenting rheumatological pathologies. However, results have not yet been driving changes in clinical practice. One reason could be that studies mainly aimed at comparing pathological gait to healthy gait, but there is stronger need for semiological descriptions of gait perturbation, severity or prognostic assessment. Furthermore, protocols used to assess gait using IMUs are too many. Likely, outcomes are highly heterogeneous and difficult to compare across large panels of studies. Therefore, homogenization is needed to foster the use of ILG to assess gait quality in neurological routine practice. The pros and cons of each protocol are emphasized so that a compromise can be reached. As well, analysis of seven complementary clinical criteria (springiness, sturdiness, smoothness, steadiness, stability, symmetry, synchronization) is advocated.

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“…This is also a typical classification method [18]. So far, several partitioning models, with different levels of granularity, have been proposed depending on the different clinical aims [19]. …”

Section: Introductionmentioning

confidence: 99%

Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors

Liu

Wang

et al. 2016

Sensors

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Gait phase is widely used for gait trajectory generation, gait control and gait evaluation on lower-limb exoskeletons. So far, a variety of methods have been developed to identify the gait phase for lower-limb exoskeletons. Angular sensors on lower-limb exoskeletons are essential for joint closed-loop controlling; however, other types of sensors, such as plantar pressure, attitude or inertial measurement unit, are not indispensable.Therefore, to make full use of existing sensors, we propose a novel gait phase recognition method for lower-limb exoskeletons using only joint angular sensors. The method consists of two procedures. Firstly, the gait deviation distances during walking are calculated and classified by Fisher’s linear discriminant method, and one gait cycle is divided into eight gait phases. The validity of the classification results is also verified based on large gait samples. Secondly, we build a gait phase recognition model based on multilayer perceptron and train it with the phase-labeled gait data. The experimental result of cross-validation shows that the model has a 94.45% average correct rate of set (CRS) and an 87.22% average correct rate of phase (CRP) on the testing set, and it can predict the gait phase accurately. The novel method avoids installing additional sensors on the exoskeleton or human body and simplifies the sensory system of the lower-limb exoskeleton.

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Gait Partitioning Methods: A Systematic Review

Cited by 287 publications

References 90 publications

Mechanical lifting energy consumption in work activities designed by means of the “revised NIOSH lifting equation”

Mechanical lifting energy consumption in work activities designed by means of the “revised NIOSH lifting equation”

Inertial Sensors to Assess Gait Quality in Patients with Neurological Disorders: A Systematic Review of Technical and Analytical Challenges

Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors

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