Artif Intell Med - Human movement onset detection from isometric force and torque measurements: a supervised pattern recognition approach.


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JECTIVE: Recent research has successfully introduced the application of robotics and mechatronics to functional assessment and motor therapy. Measurements of movement initiation in isometric conditions are widely used in clinical rehabilitation and their importance in functional assessment has been demonstrated for specific parts of the human body. The determination of the voluntary movement initiation time, also referred to as onset time, represents a challenging issue since the time window characterizing the movement onset is of particular relevance for the understanding of recovery mechanisms after a neurological damage. Establishing it manually as well as a troublesome task may also introduce oversight errors and loss of information.METHODS: The most commonly used methods for automatic onset time detection compare the raw signal, or some extracted measures such as its derivatives (i.e., velocity and acceleration) with a chosen threshold. However, they suffer from high variability and systematic errors because of the weakness of the signal, the abnormality of response profiles as well as the variability of movement initiation times among patients. In this paper, we introduce a technique to optimise onset detection according to each input signal. It is based on a classification system that enables us to establish which deterministic method provides the most accurate onset time on the basis of information directly derived from the raw signal.RESULTS: The approach was tested on annotated force and torque datasets. Each dataset is constituted by 768 signals acquired from eight anatomical districts in 96 patients who carried out six tasks related to common daily activities. The results show that the proposed technique improves not only on the performance achieved by each of the deterministic methods, but also on that attained by a group of clinical experts.CONCLUSIONS: The paper describes a classification system detecting the voluntary movement initiation time and adaptable to different signals. By using a set of features directly derived from raw data, we obtained promising results. Furthermore, although the technique has been developed within the scope of isometric force and torque signal analysis, it can be applied to other detection problems where several simple detectors are available.

Resumo Limpo

jectiv recent research success introduc applic robot mechatron function assess motor therapi measur movement initi isometr condit wide use clinic rehabilit import function assess demonstr specif part human bodi determin voluntari movement initi time also refer onset time repres challeng issu sinc time window character movement onset particular relev understand recoveri mechan neurolog damag establish manual well troublesom task may also introduc oversight error loss informationmethod common use method automat onset time detect compar raw signal extract measur deriv ie veloc acceler chosen threshold howev suffer high variabl systemat error weak signal abnorm respons profil well variabl movement initi time among patient paper introduc techniqu optimis onset detect accord input signal base classif system enabl us establish determinist method provid accur onset time basi inform direct deriv raw signalresult approach test annot forc torqu dataset dataset constitut signal acquir eight anatom district patient carri six task relat common daili activ result show propos techniqu improv perform achiev determinist method also attain group clinic expertsconclus paper describ classif system detect voluntari movement initi time adapt differ signal use set featur direct deriv raw data obtain promis result furthermor although techniqu develop within scope isometr forc torqu signal analysi can appli detect problem sever simpl detector avail

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