Publications de Hesam Sagha | EPFL Graph Search

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Technological advances in sensing and portable computing devices and wireless communication has lead to an increase in the number and variety of sensing enabled devices (e.g. smartphones or sensing garments). Pervasive computing and activity recognition sy ...

Nowadays, many systems rely on fusing different sources of information to recognize human activities and gestures, speech, or brain activities for applications in areas such as clinical practice, and health care and Human Computer Interaction (HCI). Typica ...

Detection of anomalies is a broad field of study, which is applied in different areas such as data monitoring, navigation, and pattern recognition. In this paper we propose two measures to detect anomalous behaviors in an ensemble of classifiers by monitor ...

Opportunistic sensing can be used to obtain data from sensors that just happen to be present in the user’s surroundings. By harnessing these opportunistic sensor configurations to infer activity or context, ambient intelligence environments become more rob ...

Hybrid Brain-Computer Interfaces (BCI) are representing a recent approach to develop practical BCIs. In such a system disabled users are able to use all their remaining functionalities as control possibilities in parallel with the BCI. Sometimes these peop ...

We deployed 72 sensors of 10 modalities in 15 wireless and wired networked sensor systems in the environment, in objects, and on the body to create a sensor-rich environment for the machine recognition of human activities. We acquired data from 12 subjects ...

There is a growing interest on using ambient and wearable sensors for human activity recognition, fostered by several application domains and wider availability of sensing technologies. This has triggered increasing attention on the development of robust m ...

Opportunistic activity and context recognition systems draw from the characteristic to use sensing devices according to a recognition goal at runtime that just happen to be available instead of pre-defining them at the design time of the system. Whenever a ...

Context and activity recognition in complex scenarios is prone to data loss due to disconnections, sensor failure, transmission problems, etc. This generally implies significant changes in the recognition performance. In the case of classifier fusion fault ...

Practical Brain-Computer Interfaces (BCIs) for disabled people should allow them to use all their remaining functionalities as control possibilities. Sometimes these people have residual activity of their muscles, most likely in the morning when they are n ...

Anomalies and changes in sensor networks which are deployed for activity recognition may abate the classification performance. Detection of anomalies followed by compensatory reaction would ameliorate the performance. This paper introduces a novel approach ...

We propose a method to automatically translate a pre-existing activity recognition system, devised for a source sensor domain S, so that it can operate on a newly discovered target sensor domain T , possibly of different modality. First, we use MIMO system ...

Activity recognition using onbody sensors are prone to degradation due to changes on sensor readings. The changes can occur because of degradation or alteration in the behavior of the sensor with respect to the others. In this paper we propose a method whi ...

Human activity recognition is a thriving research field. There are lots of studies in different sub-areas of activity recognition proposing different methods. However, unlike other applications, there is lack of established benchmarking problems for activi ...

One of the problems of non-invasive BCI applications is the noise and change of EEG recordings that can result in performance degradation. Signal noise and changes can for instance be the result of electrode misplacements, impedances degradation or complet ...

Activity recognition systems based on body-worn motion sensors suffer from a decrease in performance during the deployment and run-time phases, because of probable changes in the sensors (e.g. displacement or rotation), which is the case in many real-life ...

Multi-stream automatic speech recognition (MS-ASR) has been confirmed to boost the recognition performance in noisy conditions. In this system, the generation and the fusion of the streams are the essential parts and need to be designed in such a way to re ...

Brain-Computer Interfaces convey a great potential in the field of stroke rehabilitation, where the continuous monitoring of the execution of mental tasks could support the positive effects of the therapy by reinforcing specific mental patterns. We propose ...

OPPORTUNITY is project under the EU FET-Open funding1 in which we develop mobile systems to recognize human activity in dynamically varying sensor setups. The system autonomously discovers available sensors around the user and self-configures to recognize ...

Hesam Sagha

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Ensemble creation and reconfiguration for activity recognition: An information theoretic approach

Dynamic Classifier Fusion for Multi-sensory Activity Recognition

On-line anomaly detection and resilience in classifier ensembles

The OPPORTUNITY Framework and Data Processing Ecosystem for Opportunistic Activity and Context Recognition

A hybrid BCI based on the fusion of EEG and EMG activities

Collecting complex activity data sets in highly rich networked sensor environments

The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition

Dynamic Quantification of Activity Recognition Capabilities in Opportunistic Systems

A Probabilistic Approach to Handle Missing Data for Multi-Sensory Activity Recognition

Multimodal Fusion of Muscle and Brain Signals for a Hybrid-BCI

Detecting Anomalies to Improve Classification Performance in Opportunistic Sensor Networks

Kinect=IMU? Learning MIMO Signal Mappings to Automatically Translate Activity Recognition Systems Across Sensor Modalities

Detecting and rectifying anomalies in body sensor networks

Benchmarking classification techniques using the Opportunity human activity dataset

Toward online detection of BCI performance degradation

Robust Activity Recognition Combining Anomaly Detection and Classifier Retraining

Robust Activity Recognition Combining Anomaly Detection and Classifier Retraining

Stream fusion for multi-stream automatic speech recognition

EEG Microstates for BCI Therapist Feedback: Preliminary Results on a Stroke Patient

Activity Recognition in Opportunistic Sensor Environments