United States

We present a perspective of the role of participatory design in Human-Robot Collaboration (HRC) for People with Motor Disabilities (PWMD). We have been involved in several multiyear projects related to HRC for PWMD, where we encountered different challenges related to planning and participation, preferences of stakeholders, using certain participatory design techniques, technology exposure, as well as, ethical legal and social implications. These challenges helped us provide five lessons learned that could guide researchers when using participatory design with vulnerable groups. In particular, early-career researchers who start exploring research in HRC for people with disabilities.

PETRA 2021

Reflecting upon Participatory Design in Human-Robot Collaboration for People with Motor Disabilities: Challenges and Lessons Learned from Three Multiyear Projects

human-robot collaboration

people with motor disabilities

participatory design

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).

A ticket is required to attend this event, please register using the link below:

https://aamas2022-conference.auckland.ac.nz/attending/registration/

Registration Is Required

AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

technical paper

**About**
 The PErvasive Technologies Related to Assistive Environments (PETRA) conference is a highly interdisciplinary conference that focuses on computational and engineering approaches to improve the quality of life and enhance human performance in a wide range of settings, in the workplace, at home, in public spaces, urban environments, and other. Outcomes of this conference have a broad impact in application areas that include, manufacturing, transportation, healthcare, energy systems, security and safety, robotics, biomedicine, environment and conservation, and many others. PETRA brings together very different types of technologies to also address important social and healthcare issues for sensitive populations, such as the elderly, persons suffering from chronic conditions such as Alzheimer’s, Parkinson’s, Cerebral Palsy, and other disabilities or traumas.

 PETRA (which means “stone” in Greek) addresses important human living needs. From a technological perspective, PETRA motivates and inspires new scientific thrusts, innovations and discoveries to improve living environments, a particularly important issue, in an increasingly crowded and complex world where the need for inclusiveness and connectivity with the rest of the world is key. For the elderly in particular, PETRA addresses the fact that, as people grow older; they will increasingly rely on technology to be able to prolong staying in their homes and enjoy an independent life.

 The PETRA 2021 proceedings will be published in the ACM Digital Library as part of the ACM ICPS program. The ISBN number assigned to PETRA '21 is 978-1-4503-8792-7

The PErvasive Technologies Related to Assistive Environments (PETRA) conference is a highly interdisciplinary conference that focuses on computational and engineering approaches to improve the quality of life and enhance human performance in a wide range of settings, in the workplace, at home, in public spaces, urban environments, and other. Outcomes of this conference have a broad impact in application areas that include, manufacturing, transportation, healthcare, energy systems, security and safety, robotics, biomedicine, environment and conservation, and many others. PETRA brings together very different types of technologies to also address important social and healthcare issues for sensitive populations, such as the elderly, persons suffering from chronic conditions such as Alzheimer’s, Parkinson’s, Cerebral Palsy, and other disabilities or traumas.

The analysis of large data sets for several purposes is a growing requirement for many professions. Tactile charts are suitable to enable people with visual impairment and blindness performing data analysis tasks. However, few approaches focus on the development of tactile charts for data analysis purposes. Concepts are needed to enable the representation of a sufficient amount of data with tactile charts and to address arising challenges, such as information overload. In this paper, we first discuss and analyse the scalability of data represented by tactile charts, using the example of tactile scatterplots. We further address the data size limitations and propose a workflow for identifying critical, tactile representation with limited readability respecting the analysis task. Moreover, we propose concepts to increase the represented amount of data in tactile scatterplots. Based on our analysis results, we introduce tactile heatmaps as an innovative and new concept to represent data
for the haptic sense. Therefore, we developed a design for tactile heatmaps by utilising different elevation levels. We evaluated the concept as well as the feasibility of different elevation levels with 11 blind and visually impaired people. Four design conditions for embossed tactile heatmaps were compared. We further evaluated
the suitability of 3D-printed heatmaps. The results show that tactile heatmaps are suitable for representing more data than previously known tactile representation methods. They support obtaining an overview of a high amount of data and can be applied for data analysis purposes.

Tactile Heatmaps: A Novel Visualisation Technique for Data Analysis with Tactile Charts

Retinopathy of Prematurity (ROP) is a disease affecting premature infants and may lead to childhood blindness. Due to lack of trained ophthalmologists, developing a fully automated ROP diagnostic system can significantly benefit the infants affected by ROP. Based on manually segmented features, previous work produces severity scores for ROP with excellent prediction accuracy. However, when automated segmentation employed, a significant accuracy drop is observed. In this paper, we show that U-Net segmentation, which is automated, comes at no accuracy loss.

Automated ROP Diagnostic System based on Comparisons and U-Net Segmentation

In pandemic times, public facilities, such as gymnasiums and other sports facilities are closed. Hence, keeping oneself physically fit becomes particularly difficult as well as getting help from a fitness trainer or instructor is almost impossible. We propose an alternative solution, GymSoles++, a virtual trainer supporting the user with their exercise. This allows users to maintain the correct body posture to avoid injuries when performing exercises such as squats. We combine Google Glass with an unobtrusive sensing approach, an insole-based wearable system that provides feedback on the user's centre of pressure (CoP) via vibrotactile and visual aids. Previous research has shown that solely visualizing the CoP can significantly improve body posture and thus effectively assist users when performing squats and dead-lifts. In this research, we explored different feedback modalities and conclude that a vibrotactile insole is a practical and effective solution.

Combining Google Glass with Smart Insoles to Improve Body Posture when Performing Squats

Due to the rapid development of information technology, health monitoring research demands are growing tremendously. People prefer to use wearable devices that consist of sensors, which help in monitoring the health of individuals. This study aims to develop a system that remotely monitors the physiological aspects of individuals, such as blood oxygen saturation and pulse measurement, using wearable sensors and a new cloud-based fuzzy logic system, and generates necessary alarms. Monitoring health remotely based on wearable and non-invasive sensors provides a cost-effective solution, which in turn permits the elderly people to continue to live in his/her home environment as opposed to spending on expensive healthcare facilities. The developed system shall permit smooth physiological monitoring of an individual’s health by healthcare personnel in real-time. The developed system will not only assess health conditions but also provide feedback from distant facilities.
Moreover, the developed rule-based fuzzy system can infer an individual’s health condition. The study uses pulse rate and oxygen saturation data, which are processed by a fuzzy system on the cloud, and the acquired results are displayed on a mobile application.

A cloud-based pervasive application for monitoring oxygen saturation and heart rate using fuzzy-as-a-service

Detection of fake signatures is a hard task. In this paper, we present a novel method for detecting trained forgeries using features extracted from sliding windows with different overlaps on a public available dataset of static images of signatures. Using a linear machine learning model named Extreme Learning Machine (ELM), our methodology achieves, in average, an Equal Error Rates (EER) of 2.31% for an overlap of 90%. In line with the state-of-the-art results available in the scientific literature.

Handwriting features based detection of fake signatures

In domains such as Human-Robot Collaboration artificial agents must be able to support mutual adaptation and learning. Towards this direction, we use a discrete Soft Actor-Critic agent on a real-time collaborative game with humans. We examine how different allocations of on-line and off-line gradient updates impact the game performance and the total training time. Our results suggest that early allocation of a high number of off-line g/u can accelerate learning while shortening training duration.

Accelerating Human-Agent Collaborative Reinforcement Learning

In this presentation, we propose Structurally Guided Channel Attention Networks (SGCA-Net), a principled way to guide the channel at- tention of CNNs. Convolution operator constructs features maps by using both channel and spatial information within the receptive fields of its filters. Prior research has investigated the impact of strengthening the representational power of CNNs using channel attention modules. In this work, we guide the channel attention of networks using feature vectors that contain clinically relevant information. We do so by attaching guided attention modules into a state-of-the-art network architecture, and guiding these attention modules with domain knowledge using feature vectors. Experi- ments on a dataset of 5512 posterior retinal images, taken using a wide angle fundus camera, show that SGCA-Net achieves 0.983 and 0.948 AUC to predict plus and normal categories, respectively. SGCA-Net achieves higher performance than CNNs without atten- tion modules and CNNs with unguided attention modules.

Structurally Guided Channel Attention Networks: SGCA-Net

Currently there is much research in the area of person identification. Mostly it is based on multi-biometric data. In this paper, we aim to leverage soft biometric properties to achieve person reidentification by using unobtrusive sensors, envisioning assistive environments, which recognize their user and thus automatically personalize and adapt. In practice, a car seat recognizes the person
who sits down and greets the person with their own name, enabling various customisation in the car unique to the user, like seat configurations.
We present a system composed of a sensor equipped car seat, which is able to recognize a person from a predefined group. We contribute two classification approaches based on cosine similarity measure and on triplet loss learning. These are thoroughly analysed and evaluated in a user study with nine participants. We achieve the best re-identification performance using a hand-crafted feature approach based on the comparing measure of cosine similarity combined with majority voting. The highest overall precision achieved in re-identifying a person from a group of nine users is 80 %.

Person Re-Identification in a Car Seat: Comparison of Cosine Similarity and Triplet Loss based approaches on Capacitive Proximity Sensing data

People with severe motor impairment oftentimes rely on unconventional assistive interfaces to complete daily activities. In this work, we developed a holistic shared control framework to help them achieve fast and accurate indoor navigation using low throughput human machine interfaces.
Based off our previously developed shared position control method,
we added orientation control, action control, and a state machine architecture to create the new framework NOVELTI 2.0.
The framework provides generic support for various LTIs. We validated the framework using Emotiv EPOC headset and a single-switch interface.
A comprehensive comparative study involving 10 human subjects navigating a wheelchair in a real indoor environment demonstrated that our framework could 
achieve high navigation accuracy about 0.05 m for position and 5 degrees for orientation.
Compared to steering control, our method required half the navigation time and half the number of commands in routes longer than 20 m.
The NASA TLX survey results also indicated lower subjective workload with our method.
Experimental data is provided for download and future evaluation.

NOVELTI2.0: Enhanced Assistive Robot Navigation with Low Throughput Interfaces

n this paper, we present a novel method to learn end-to-end vi-suomotor policies for robotic manipulators. The method computesstate-action mappings in a supervised learning manner from videodemonstrations and robot trajectories. We show that the robotlearns to perform different tasks by associating image features withthe corresponding movement primitives of different grasp poses.To evaluate the effectiveness of the proposed learning method, weconduct experiments with a PR2 robot in a simulation environment.The purpose of these experiments is to evaluate the system’s abilityto perform manipulation tasks

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Descriptive and Prescriptive Visual Guidance to Improve Shared Situational Awareness in Human-Robot Teaming

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