In many Intelligent Transportation System (ITS) applications that crowd-source data from probe vehicles, a crucial step is to accurately map the GPS trajectories to the road network in real time. This process, known as map-matching, often needs to account for noise and sparseness of the data because (1) highly precise GPS traces are rarely available, and (2) dense trajectories are costly for live transmission and storage.

/pdf/online-map-matching-based-on-hidden-markov-model-for-real-5b98nb56fi.pdf

Online map-matching based on Hidden Markov model for real-time traffic sensing applications

RiSH: A robot-integrated smart home for elderly care

Motion- and location-based online human daily activity recognition

This article presents a gesture recognition/adaptation system for human--computer interaction applications that goes beyond activity classification and that, as a complement to gesture labeling, characterizes the movement execution. We describe a template-based recognition method that simultaneously aligns the input gesture to the templates using a Sequential Monte Carlo inference technique. Contrary to standard template-based methods based on dynamic programming, such as Dynamic Time Warping, the algorithm has an adaptation process that tracks gesture variation in real time. The method continuously updates, during execution of the gesture, the estimated parameters and recognition results, which offers key advantages for continuous human--machine interaction. The technique is evaluated in several different ways: Recognition and early recognition are evaluated on 2D onscreen pen gestures; adaptation is assessed on synthetic data; and both early recognition and adaptation are evaluated in a user study involving 3D free-space gestures. The method is robust to noise, and successfully adapts to parameter variation. Moreover, it performs recognition as well as or better than nonadapting offline template-based methods.

/pdf/adaptive-gesture-recognition-with-variation-estimation-for-uth0ex5b5w.pdf

Adaptive Gesture Recognition with Variation Estimation for Interactive Systems

In this paper, we present an approach to the online implementation of a gait event detector based on machine learning algorithms. Gait events were detected using a uniaxial gyro that measured the foot instep angular velocity in the sagittal plane to feed a four-state left-right hidden Markov model (HMM). The short-time Viterbi algorithm was used to overcome the limitation of the standard Viterbi algorithm, which does not allow the online decoding of hidden state sequences. Supervised learning of the HMM structure and validation with the leave-one-subject-out validation method were performed using treadmill gait reference data from an optical motion capture system. The four gait events were foot strike, flat foot (FF), heel off (HO), and toe off. The accuracy ranged, on average, from 45 ms (early detection, FF) to 35 ms (late detection, HO); the latency of detection was less than 100 ms for all gait events but the HO, where the probability that it was greater than 100 ms was 25%. Overground walking tests of the HMM-based gait event detector were also successfully performed.

Online Decoding of Hidden Markov Models for Gait Event Detection Using Foot-Mounted Gyroscopes

In this paper we present the implementation of an online HMM decoding process. The algorithm derives an online version of the Viterbi algorithm on successive variable length windows, iteratively storing portions of the optimal state path. We explicit the relation between the hidden layer's topology and the applicability and performance prediction of the algorithm. We evaluate the validity and performance of the algorithm on a phone recognition task on a database of continuous speech from a native French speaker. We specifically study the latency-accuracy performance of the algorithm.

/pdf/short-time-viterbi-for-online-hmm-decoding-evaluation-on-a-12bghpzcjj.pdf

Short-time Viterbi for online HMM decoding: Evaluation on a real-time phone recognition task

We present an approach to model the temporal evolution of audio descriptors using Segmental Models (SMs). This method allows to segment a signal as a sequence of primitives, constituted by a set of trajectories defined by the user. This allows one to explicitly model the time duration of primitives, and to take into account the time dependence between successive signal frames, contrary to standard Hidden Markov Models. We applied this approach to a database of violin playing. Various types of glissando and dynamics variations were specifically recorded. Our results shows that our approach using Segmental Models provides a segmentation that can be easily interpreted. Quantitatively, the Segmental Models performed better than standard implementation of Hidden Markow Models.

https://hal.archives-ouvertes.fr/hal-01161223/document

Towards morphological sound description using segmental models

http://articles.ircam.fr/textes/Bloit10a/index.pdf

Modeling and segmentation of audio descriptor profiles with segmental models

We present Urban Musical Game, an installation using augmented sports balls to manipulate and transform an interactive music environment. The interaction is based on playing techniques, a concept borrowed from traditional music instruments and applied here to non musical objects.

/pdf/the-urban-musical-game-using-sport-balls-as-musical-3hdjey0akr.pdf

The urban musical game: using sport balls as musical interfaces

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Papers

Short-time Viterbi for online HMM decoding: Evaluation on a real-time phone recognition task

Towards morphological sound description using segmental models

Modeling and segmentation of audio descriptor profiles with segmental models

The urban musical game: using sport balls as musical interfaces