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Action detection fusing multiple Kinects and a WIMU: an application to in-home assistive technology for the elderly

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Abstract

We present a vision-inertial system which combines two RGB-Depth devices together with a wearable inertial movement unit in order to detect activities of the daily living. From multi-view videos, we extract dense trajectories enriched with a histogram of normals description computed from the depth cue and bag them into multi-view codebooks. During the later classification step a multi-class support vector machine with a RBF-\(\mathcal {X}^2\) kernel combines the descriptions at kernel level. In order to perform action detection from the videos, a sliding window approach is utilized. On the other hand, we extract accelerations, rotation angles, and jerk features from the inertial data collected by the wearable placed on the user’s dominant wrist. During gesture spotting, a dynamic time warping is applied and the aligning costs to a set of pre-selected gesture sub-classes are thresholded to determine possible detections. The outputs of the two modules are combined in a late-fusion fashion. The system is validated in a real-case scenario with elderly from an elder home. Learning-based fusion results improve the ones from the single modalities, demonstrating the success of such multimodal approach.

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Notes

  1. This is not stated in the published manuscript, but in an errata document. Check [46] and the errata document for more detail: http://jhmdb.is.tue.mpg.de/show_file?filename=Errata_JHMDB_ICCV_2013.pdf.

  2. The concatenation of the Viewpoint Feature Histogram (VFH) and Camera Roll Histogram (CRH).

  3. Models are a class-representative instance that will be used to compare test gestures in order to compute DTW matrices, see Sect. 4.2.4 for more details.

  4. Dense optical flow is computed using [32].

  5. These two quantities define the size of the dynamic time warping matrix, i.e., \(l_g \times l_M\)

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Acknowledgements

This work was partly supported by the spanish project TIN2016-74946-P and CERCA Programme / Generalitat de Catalunya. The work of Albert Clapés was supported by SUR-DEC of the Generalitat de Catalunya and FSE. We would also like to thank the SARQuavitae Claret elder home and all the people who volunteered for the recording of the dataset.

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Authors and Affiliations

  1. Matemátiques i Informática, UB, Gran Via de les Corts Catalanes 585, 08007, Barcelona, Spain

    Albert Clapés, Àlex Pardo, Oriol Pujol Vila & Sergio Escalera

  2. Computer Vision Center, Campus UAB, Edifici O, 08193, Cerdanyola del Vallès, Spain

    Albert Clapés & Sergio Escalera

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  1. Albert Clapés
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  2. Àlex Pardo
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  3. Oriol Pujol Vila
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  4. Sergio Escalera
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Correspondence to Sergio Escalera.

Appendix: Wearable Module feature comparison

Appendix: Wearable Module feature comparison

In Sect. 4.2, several features are detailed. They are used to describe the gestures performed among different magnitudes. Here, an extensive comparison between different features’ combinations is presented, with the objective of demonstrating which is the most suitable for performing gesture recognition using the SAR-Quavitae Claret dataset.

Fig. 19
figure 19

In a, raw accelerometer \(+\) jerk. In b, sorted accelerometer \(+\) complementary filter

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Fig. 20
figure 20

In a, sorted accelerometer \(+\) jerk. In b, complementary filter + jerk

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Fig. 21
figure 21

In a, raw accelerometer \(+\) sorted accelerometer. In b, raw accelerometer + complementary accelerometer

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Fig. 22
figure 22

In a, raw accelerometer \(+\) sorted accelerometer \(+\) complementary filter. In b, raw accelerometer \(+\) sorted accelerometer \(+\) jerk

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Fig. 23
figure 23

In a, sorted accelerometer \(+\) complementary filter \(+\) jerk. In b, raw accelerometer \(+\) sorted accelerometer \(+\) complementary filter \(+\) jerk

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Given the set of features described in the article, all their possible combinations have been generated. For each feature set, we have computed the distances between each of the pre-segmented gestures, using DTW as a metric.

Each of the matrices represent the average distance between segmented gestures, using leave-one-subject-out strategy. That is, for each subject, all his gestures are compared to the remaining ones. Finally, the average of all the distances is computed. The objective is to find the combination that is more discriminative. This means that, the best features will be those that minimize the distance between equal classes (diagonal of the matrices), while maximizing the distance against different classes.

In most of the combinations showed, one can see that “taking-pill” and “turn-page” gestures are easily confused, while “drink” and “spoonful” are, most of the times, distant from the other classes. Looking the matrices with more detail, Figs. 19b, 20a, b, and 23a lead us to the conclusion that Raw accelerometer is crucial for representing the data correctly. Regarding to those combinations only using a pair of features, Figures 21a, b, and 19a show us that these combinations are usually enough to discriminate correctly “drink” and “spoonful” classes, but are not enough for “taking-pill” and “turn-page.” The same happens in Figs. 22a, b, where “taking-pill” and “turn-page” are confused with “spoonful.” The combination that is able to discriminate correctly “turn-page,” “drink,” and “spoonful” is the one shown in Fig. 23b. However, “taking-pill” is still close to the other classes. This is the effect of the variability in the performance of the gesture.

Finally, thanks to this deep analysis, we can state that the most suitable combination of features for the presented dataset is the one including raw accelerometer, sorted accelerometer, complementary filter, and Jerk. However, the distance matrices presented in this section glimpses some issues related to the intra-class variability. Nevertheless, our hypothesis is that this is due to the reduced set of data available, that hinders the representativeness of the gestures.

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Clapés, A., Pardo, À., Pujol Vila, O. et al. Action detection fusing multiple Kinects and a WIMU: an application to in-home assistive technology for the elderly. Machine Vision and Applications 29, 765–788 (2018). https://doi.org/10.1007/s00138-018-0931-1

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