The aim of the present study involves designing a humanoid robot guide as a walking trainer for elderly and rehabilitation patients. The system is based on the humanoid robot Pepper with a compliance approach that allows to match the motion intention of the user to the robot's pace. This feasibility study is backed up by an experimental evaluation conducted in a rehabilitation centre. We hypothesize that Pepper robot used as an assistive partner, can also benefit elderly users by motivating them to perform physical activity.
However, very few approaches develop physically assistive navigation by the usage of humanoid robots while exploiting its social component. Therefore, further studies need to be conducted considering the needs of chronically ill patients and elderly individuals, by proposing and validating a suitable robotic platform. In this way, investigating the proxemics preferences in this category will allow to provide an effective motivational behaviour, safe model and a system able to capture gait parameters @cite_0 .
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The impact of the world-wide ageing population has commenced with respect to society in developed countries. Several researchers focused on exploring new methods to improve the quality of life of elderly individuals by allowing them to remain independent and healthy to the maximum possible extent. For example, new walking aids are designed to allow elderly individuals to remain mobile in a safe manner because the importance of walking is well-known. The aim of the present study involves designing a humanoid robot guide as a walking trainer for elderly individuals. It is hypothesized that the same service robot provides an assistive and social contribution with respect to interaction between elderly users by motivating them to walk more and simultaneously provides assistance, such as physical assistance and gait monitoring, while walking. This study includes a detailed statement of the research problem as well as a literature review of existing studies related to walking companion robots. A user-centred design approach is adopted to report the results of the current first feasibility study by using a commercially available humanoid robot known as Pepper developed by Softbank-Aldebaran. A quantitative questionnaire was used to investigate all elements that assess intrinsic motivation in users while performing a given activity. Conversely, basic gait data were acquired through a video analysis to test the capability of the robot to modify the gait of human users. The results in terms of the feedback received from elderly subjects and the literature review improve the design of the walking trainer for elderly individuals. Mobility assistance robots provide support to elderly or patients during walking. The design of a safe and intuitive assistance behavior is one of the major challenges in this context. We present an integrated approach for the context-specific, on-line adaptation of the assistance level of a rollator-type mobility assistance robot by gain-scheduling of low-level robot control parameters. A human-inspired decision-making model, the drift-diffusion Model, is introduced as the key principle to gain-schedule parameters and with this to adapt the provided robot assistance in order to achieve a human-like assistive behavior. The mobility assistance robot is designed to provide (a) cognitive assistance to help the user following a desired path towards a predefined destination as well as (b) sensorial assistance to avoid collisions with obstacles while allowing for an intentional approach of them. Further, the robot observes the user long-term performance and fatigue to adapt the overall level of (c) physical assistance provided. For each type of assistance a decision-making problem is formulated that affects different low-level control parameters. The effectiveness of the proposed approach is demonstrated in technical validation experiments. Moreover, the proposed approach is evaluated in a user study with 35 elderly persons. Obtained results indicate that the proposed gain-scheduling technique incorporating ideas of human decision-making models shows a general high potential for the application in adaptive shared control of mobility assistance robots. In todays aging society, many people require mobility assistance, that can be provided by robotized assistive wheelchairs with a certain degree of autonomy when manual control is unfeasible due to disability. Robot wheelchairs, though, are not supposed to be completely in control because lack of human intervention may lead to loss of residual capabilities and frustration. Most of these systems rely on shared control, which typically consists of swapping control from human to robot when needed. However, this means that persons never deal with situations they find difficult. We propose a new shared control approach to allow constant cooperation between humans and robots, so that assistance may be adapted to the user's skills. Our proposal is based on the reactive navigation paradigm, where robot and human commands become different goals in a Potential Field. Our main novelty is that human and robot attractors are weighted by their respective local efficiencies at each time instant. This produces an emergent behavior that combines both inputs in an efficient, safe and smooth way and is dynamically adapted to the user's needs. The proposed control scheme has been successfully tested at hospital Fondazione Santa Lucia (FSL) in Rome with several volunteers presenting different disabilities. For an effective intelligent active mobility assistance robot, the walking pattern of a patient or an elderly person has to be analyzed precisely. A well-known fact is that the walking patterns are gaits, that is, cyclic patterns with several consecutive phases. These cyclic motions can be modeled using the consecutive gait phases. In this paper, we present a completely non-invasive framework for analyzing a normal human walking gait pattern. Our framework utilizes a laser range finder sensor to collect the data, a combination of filters to preprocess these data, and an appropriately synthesized Hidden Markov Model (HMM) for state estimation, and recognition of the gait data. We demonstrate the applicability of this setup using real data, collected from an ensemble of different persons. The results presented in this paper demonstrate that the proposed human data analysis scheme has the potential to provide the necessary methodological (modeling, inference, and learning) framework for a cognitive behavior-based robot control system. More specifically, the proposed framework has the potential to be used for the recognition of abnormal gait patterns and the subsequent classification of specific walking pathologies, which is needed for the development of a contextaware robot mobility assistant.