Preference-Based Learning for Exoskeleton Gait Optimization

This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of transport, our approach directly learns from user preferences, e.g., for comfort. Building upon work in preference-based interactive learning, we present the CoSpar algorithm. CoSpar prompts the user to give pairwise preferences between trials and suggest improvements; as exoskeleton walking is a non-intuitive behavior, users can provide preferences more easily and reliably than numerical feedback. We show that CoSpar performs competitively in simulation and demonstrate a prototype implementation of CoSpar on a lower-body exoskeleton to optimize human walking trajectory features. In the experiments, CoSpar consistently found user-preferred parameters of the exoskeleton’s walking gait, which suggests that it is a promising starting point for adapting and personalizing exoskeletons (or other assistive devices) to individual users

Partners for Change: Community Residents and Agencies

Efforts to combine the knowledge and skills of community residents and diverse professionals to bring about community and service delivery change are becoming increasingly popular, yet difficult to achieve. This article details, from the perspective of community residents and agency and university staff, the challenges, strategies, and benefits in developing one community-agency collaborative which has successfully engaged community residents. The program is located in a low-income, culturally-diverse, densely populated urban area. Challenges faced by the partnership included recruiting residents, reducing logistical barriers to resident involvement, joining together residents and agency staff, and aligning community and agency goals. Successful strategies in overcoming these challenges included responding quickly to community concerns, developing more personal recruiting strategies, changing logistics to enhance resident participation, increasing program visibility in the community, creating shared goals and vision, and training. Observed benefits include community residents believe their participation has resulted in personal, agency and neighborhood improvements and increased cross-cultural understanding

A Checklist to Publish Collections as Data in GLAM Institutions

Large-scale digitization in Galleries, Libraries, Archives and Museums (GLAM) created the conditions for providing access to collections as data. It opened new opportunities to explore, use and reuse digital collections. Strong proponents of collections as data are the Innovation Labs which provided numerous examples of publishing datasets under open licenses in order to reuse digital content in novel and creative ways. Within the current transition to the emerging data spaces, clouds for cultural heritage and open science, the need to identify practices which support more GLAM institutions to offer datasets becomes a priority, especially within the smaller and medium-sized institutions. This paper answers the need to support GLAM institutions in facilitating the transition into publishing their digital content and to introduce collections as data services; this will also help their future efficient contribution to data spaces and cultural heritage clouds. It offers a checklist that can be used for both creating and evaluating digital collections suitable for computational use. The main contributions of this paper are i) a methodology for devising a checklist to create and assess digital collections for computational use; ii) a checklist to create and assess digital collections suitable for use with computational methods; iii) the assessment of the checklist against the practice of institutions innovating in the Collections as data field; and iv) the results obtained after the application and recommendations for the use of the checklist in GLAM institutions

A checklist to publish collections as data in GLAM institutions

Purpose The purpose of this study is to offer a checklist that can be used for both creating and evaluating digital collections, which are also sometimes referred to as data sets as part of the collections as data movement, suitable for computational use. Design/methodology/approach The checklist was built by synthesising and analysing the results of relevant research literature, articles and studies and the issues and needs obtained in an observational study. The checklist was tested and applied both as a tool for assessing a selection of digital collections made available by galleries, libraries, archives and museums (GLAM) institutions as proof of concept and as a supporting tool for creating collections as data. Findings Over the past few years, there has been a growing interest in making available digital collections published by GLAM organisations for computational use. Based on previous work, the authors defined a methodology to build a checklist for the publication of Collections as data. The authors’ evaluation showed several examples of applications that can be useful to encourage other institutions to publish their digital collections for computational use. Originality/value While some work on making available digital collections suitable for computational use exists, giving particular attention to data quality, planning and experimentation, to the best of the authors’ knowledge, none of the work to date provides an easy-to-follow and robust checklist to publish collection data sets in GLAM institutions. This checklist intends to encourage small- and medium-sized institutions to adopt the collection as data principles in daily workflows following best practices and guidelines