Improving Emotion Detection with Sub-clip Boosting

Toto, Ermal; Foley, Brendan J.; Rundensteiner, Elke A.

doi:10.1007/978-3-030-10997-4_3

Cited by 2 publications

(1 citation statement)

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“…For the sequence P b a , however, we do not take into account the temporal ordering of its values, and we only consider the values of p e and p o , as illustrated in Figure 3b. We apply to the isolated primitives four different aggregation techniques, three of which were originally designed by Toto, Foley, and Rundensteiner for audio clip classification (Toto, Foley, and Rundensteiner 2019).…”

Section: Segment Classification: Prediction Boostingmentioning

confidence: 99%

Combining Real-Time Segmentation and Classification of Rehabilitation Exercises with LSTM Networks and Pointwise Boosting

Bevilacqua

Ciampi

Argent

et al. 2020

AAAI

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Autonomous biofeedback tools in support of rehabilitation patients are commonly built as multi-tier pipelines, where a segmentation algorithm is first responsible for isolating motion primitives, and then classification can be performed on each primitive. In this paper, we present a novel segmentation technique that integrates on-the-fly qualitative classification of physical movements in the process. We adopt Long Short-Term Memory (LSTM) networks to model the temporal patterns of a streaming multivariate time series, obtained by sampling acceleration and angular velocity of the limb in motion, and then we aggregate the pointwise predictions of each isolated movement using different boosting methods. We tested our technique against a dataset composed of four common lower-limb rehabilitation exercises, collected from heterogeneous populations (clinical and healthy). Experimental results are promising and show that combining segmentation and classification of orthopaedic movements is a valid method with many potential real-world applications.

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Section: Segment Classification: Prediction Boostingmentioning

confidence: 99%