Recommender System for Responsive Engagement of Senior Adults in Daily Activities

Understanding and predicting how people change their behavior after an intervention from time series data is an important task for health recommender systems. This task is especially challenging when the time series data is frequently sampled. In this paper, we develop and propose a novel recommender system that aims to promote physical activeness in elderly people. The main novelty of our recommender system is that it learns how senior adults with different lifestyle change their activeness after a digital health intervention from minute-by-minute fitness data in an automated way. We trained the system and validated the recommendations using data from senior adults. We demonstrated that the low-level information contained in time series data is an important predictor of behavior change. The insights generated by our recommender system could help senior adults to engage more in daily activities.

Shoaling with fish: using miniature robotic agents to close the interaction loop with groups of zebrafish Danio rerio

Frank Bonnet

Robotic animals are nowadays developed for various types of research, such as bio-inspired robotics, biomimetics and animal behavior studies. The miniaturization of technologies and the increase in performance of embedded systems allowed engineers to develop more powerful, sophisticated and miniature devices. The case of robotic fish is a typical example of such challenging design: the fish locomotion and body movements are difficult to reproduce and the device has to move autonomously underwater. More specifically, in the case of collective animal behavior research, the robotic device has to interact with animals by generating and exploiting signals relevant for social behavior. Once perceived by the animal society as conspecific, these robots can become powerful tools to study the animal behaviors, as they can at the same time monitor the changes in behavior and influence the collective choices of the animal society. In this work, we present novel robotized tools that can integrate shoals of fish in order to study their collective behaviors. This robotic platform is composed of two subsystems: a miniature wheeled mobile robot that can achieve dynamic movements and multi-robot long-duration experiments, and a robotic fish lure that is able to beat its tail to generate fish-like body movements. The two subsystems are coupled with magnets which allows the wheeled mobile robot to steer the robotic fish lure so that it reaches very high speeds and accelerations while achieving shoaling. An experimental setup to conduct studies on mixed societies of artificial and living fish was designed to facilitate the experiments for biologists. A software framework was also implemented to control the robots in a closed-loop using data extracted from visual tracking that retrieved the position of the robots and the fish. We selected the zebrafish Danio rerio as a model to perform experiments to qualify our system. We used the current state of the art on the zebrafish social behavior to define the specifications of the robots, and we performed stimuli analysis to improve their developments. Bio-inspired controllers were designed based on data extracted from experiments with zebrafish for the robots to mimic the zebrafish locomotion underwater. Experiments involving a robot with a shoal of fish in a constrained environment showed that the locomotion of the robot was one of the main factor to affect the collective behavior of zebrafish. We also shown that the body movements and the biomimetic appearance of the lure could increase its acceptance by fish. Finally, an experiment involving a mixed society of fish and robots qualified the robotic system to be integrated among a zebrafish shoal and to be able to influence the collective decisions of the fish. These results are very promising for the field of fish-robot interaction studies, as we showed the effect of the robots in long-duration experiments and repetitively, with the same order of response from the animals.

EPFL2017

Recommender Systems for Healthy Behavior Change

Onur Yürüten

Sedentary lifestyles and bad eating habits influence the onset of many serious health problems. Healthy behavior change is an arduous task, and requires a careful planning. In this thesis, we propose that behavior recommenders can help their users achieve healthy behavior change. Such a system should inspire its users with small, incremental and achievable goals. For this, it must resolve a trade-off between two opposing objectives: help the user achieve a steady improvement in target behavior, and avoid extreme goals that may injure or discourage the user. This is an unprecedented challenge in the recommender systems research. If the system understands the impacts of past interventions for behavior change, it can determine its usersâ behavioral responses to its own recommendations. This implies a specific data curation, in which we not only measure people's behavior but also deliberately introduce an intervention to monitor its effect on people's patterns. In turn, the system can use these existing users' information to derive the right procedure for effective recommendations. In this study we capitalize on this insight and develop InspiRE - our behavior recommender framework. Through InspiRE we propose the following contributions: 1) We design the data curation. 2) We develop the novel approaches for behavior profiling 3) We develop an evaluation process for this novel type of recommender system, and also compare it with traditional, similarity-based recommendation approach. We curate a dataset that contains information of daily step counts and social intervention for 83 people. InspiRE successfully uses the observations from this dataset, and proposes recommendations that are both effective and feasible. We also show that InspiRE can generalize to other dimensions of well being: we demonstrate this through a dataset that contains the snacking patterns of 73 people, who receive message-based interventions. We observe that InspiRE's recommendation strategy is in line with theories of behavior change.

EPFL2017

Personalizable intervention systems to promote healthy behavior change

Igor Kulev

Adopting healthy behaviors can prevent the onset of many adverse health conditions. However, behavior changes are difficult to make, and often, people who like to improve their behaviors do not know how to do that. Personalizable intervention systems could assist them to achieve healthy behavior change. These systems decide what would be the optimal intervention for the target user based on his or her characteristics, including current and past behavior patterns. In this thesis, we propose novel solutions that address the main challenges in building a personalizable intervention system to promote healthy behavior change. First, we propose a system based on a Bayesian mixture model to identify subpopulations with different behavior changes from longitudinal data. This system is especially suitable when the amount of data is limited, and when there are unobserved factors that might affect behavior change. Second, we propose CLINT, a system based on a latent-variable model, to discover and predict behavior change patterns from fine-grained sensor data. The novelty of this system is that it produces interpretable patterns that could be used to suggest successful behavior change strategies from the existing users similar to the target user. Third, we propose a personalizable intervention system to improve the physical activeness of senior adults. The main novelty of this system is that it uses historical time series fitness data to decide which intervention to recommend. Finally, we propose ACFR, an adversarial approach to reduce intervention bias in observational data. This approach learns a balanced representation of the covariates that allows personalizable intervention systems to make a better estimate of the intervention effect. Our solutions turn existing human behavior data into actionable insights for future users who may have unhealthy lifestyles.

EPFL2020

Shoaling with fish: using miniature robotic agents to close the interaction loop with groups of zebrafish Danio rerio

Frank Bonnet

EPFL2017

Recommender Systems for Healthy Behavior Change

Onur Yürüten

EPFL2017