| 1. |
- Coppola, Claudio, et al.
(författare)
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Applying a 3D qualitative trajectory calculus to human action recognition using depth cameras
- 2015
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Konferensbidrag (refereegranskat)abstract
- The life span of ordinary people is increasing steadily and many developed countries are facing the big challenge of dealing with an ageing population at greater risk of impairments and cognitive disorders, which hinder their quality of life. Monitoring human activities of daily living (ADLs) is important in order to identify potential health problems and apply corrective strategies as soon as possible. Towards this long term goal, the research here presented is a first step to monitor ADLs using 3D sensors in an Ambient Assisted Living (AAL) environment. In particular, the work here presented adopts a new 3D Qualitative Trajectory Calculus (QTC3D) to represent human actions that belong to such activities, designing and implementing a set of computational tools (i.e. Hidden Markov Models) to learn and classify them from standard datasets. Preliminary results show the good performance of our system and its potential application to a large number of scenarios, including mobile robots for AAL.
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| 2. |
- Kucner, Tomasz Piotr, et al.
(författare)
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Survey of maps of dynamics for mobile robots
- 2023
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Ingår i: The international journal of robotics research. - : Sage Publications. - 0278-3649 .- 1741-3176. ; 42:11, s. 977-1006
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Tidskriftsartikel (refereegranskat)abstract
- Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.
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| 3. |
- Martinez Mozos, Oscar, 1974-, et al.
(författare)
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Stress Detection Using Wearable Physiological and Sociometric Sensors
- 2017
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Ingår i: International Journal of Neural Systems. - Singapore : World Scientific. - 0129-0657 .- 1793-6462. ; 27:2
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Tidskriftsartikel (refereegranskat)abstract
- Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and k-nearest neighbor. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real-time stress detection. Finally, we present an study of the most discriminative features for stress detection.
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| 4. |
- Sandulescu, Virginia, et al.
(författare)
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Stress Detection Using Wearable Physiological Sensors
- 2015
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Ingår i: Artificial Computation in Biology and Medicine. - Cham : Springer. - 9783319189130 - 9783319189147 ; , s. 526-532
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Konferensbidrag (refereegranskat)abstract
- As the population increases in the world, the ratio of health carers is rapidly decreasing. Therefore, there is an urgent need to create new technologies to monitor the physical and mental health of people during their daily life. In particular, negative mental states like depression and anxiety are big problems in modern societies, usually due to stressful situations during everyday activities including work. This paper presents a machine learning approach for stress detection on people using wearable physiological sensors with the final aim of improving their quality of life. The presented technique can monitor the state of the subject continuously and classify it into ”stressful” or ”non-stressful” situations. Our classification results show that this method is a good starting point towards real-time stress detection.
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