English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Automatic recognition of the quality of movement in human beings is achallenging task, given the difficulty both in defining the constraints thatmake a movement correct, and the difficulty in using noisy data to determine ifthese constraints were satisfied. This paper presents a method for thedetection of deviations from the correct form in movements from physicaltherapy routines based on Hidden Markov Models, which is compared to DynamicTime Warping. The activities studied include upper an lower limbs movements,the data used comes from a Kinect sensor. Correct repetitions of the activitiesof interest were recorded, as well as deviations from these correct forms. Theability of the proposed approach to detect these deviations was studied.Results show that a system based on HMM is much more likely to determine if acertain movement has deviated from the specification.

HMM and DTW for evaluation of therapeutical gestures using kinect