Momotaz Begum

Performance of daily activities by older adults with dementia: The role of an assistive robot

Older adults with cognitive impairment often have difficulties in remembering the proper sequence of activities of daily living (ADLs) or how to use the tools necessary to perform ADLs. They, therefore, require reminders in a timely fashion while performing ADLs. This is a very stressful situation for the caregivers of people with dementia. In this paper we describe a pilot study where a tele-operated assistive robot helps a group of older adults with dementia (OAwD) to perform an ADL, namely making a cup of tea in the kitchen. Five OAwD along with their caregivers participated in this study which took place in a simulated-home setting. The purpose of this study was to investigate the feasibility and usability of a robotic system in assisting the OAwD to perform ADL in a home setting. The findings from this study will contribute to achieve our ultimate goal of designing a full-fledged assistive robot that assists OAwD aging in their own homes. The assistive robots designed for people with dementia mostly focus on companionship. This is, to the best of our knowledge, the first attempt to design an assistive robot which will provide step-by-step guidance to people with dementia in their activities of daily living.

Are Robots Ready to Deliver Autism Interventions? A Comprehensive Review

This article presents a review of the contemporary robotics research with respect to making robots and human–robot interaction (HRI) useful for autism intervention in clinical settings. Robotics research over the past decade has demonstrated that many children with autism spectrum disorders (ASDs) have a strong interest in robots and robot toys and can connect with a robot significantly better than with a human. Despite showing great promise, research in this direction has made minimal progress in advancing robots as clinically useful for ASD intervention. Moreover, the clinicians are generally not convinced about the potential of robots. A major reason behind this is that a vast majority of HRI studies on robot-mediated intervention (RMI) do not follow any standard research design and, consequently, the data produced by these studies is minimally appealing to the clinical community. In clinical research on ASD intervention, a systematic evaluation of the evidence found from a study is performed to determine the effectiveness of an experimental intervention (e.g., a RMI). An intervention that produces a stable positive effect is considered as an evidence-based practice (EBP) in autism. EBPs enable clinicians to choose the best available treatments for an individual with ASD. The ultimate goal of RMI, therefore, is to be considered as an EBP so that they can actually be used for treating autism. There are several criteria to measure the strength of evidence, and they are mostly geared toward rigorous research design. The research on RMI, therefore, needs to follow standard research design to be acceptable by the clinical community. This paper reviews the contemporary literature on robotics and autism to understand the status of RMI with respect to being an EBP in autism treatment. First, a set of guidelines is reported which is considered as a benchmark for research design in clinical research on ASD intervention and can easily be adopted in HRI studies on RMI. The existing literature on RMI is then reviewed with respect to these guidelines. We hope that the guidelines reported in this paper will help the robotics community to design user studies on RMI that meet clinical standards and thereby produce results that can lead RMI toward being considered as an EBP in autism. Note that the paper is exclusively focused on the role of robots in ASD intervention/therapy. Reviews on the use of robots in ASD diagnosis are beyond the scope of this paper.

Robots to assist daily activities: views of older adults with Alzheimer's disease and their caregivers.

BACKGROUND Robots have the potential to both enable older adults with dementia to perform daily activities with greater independence, and provide support to caregivers. This study explored perspectives of older adults with Alzheimers disease (AD) and their caregivers on robots that provide stepwise prompting to complete activities in the home. METHODS Ten dyads participated: Older adults with mild-to-moderate AD and difficulty completing activity steps, and their family caregivers. Older adults were prompted by a tele-operated robot to wash their hands in the bathroom and make a cup of tea in the kitchen. Caregivers observed interactions. Semi-structured interviews were conducted individually. Transcribed interviews were thematically analyzed. RESULTS Three themes summarized responses to robot interactions: contemplating a future with assistive robots, considering opportunities with assistive robots, and reflecting on implications for social relationships. Older adults expressed opportunities for robots to help in daily activities, were open to the idea of robotic assistance, but did not want a robot. Caregivers identified numerous opportunities and were more open to robots. Several wanted a robot, if available. Positive consequences of robots in caregiving scenarios could include decreased frustration, stress, and relationship strain, and increased social interaction via the robot. A negative consequence could be decreased interaction with caregivers. CONCLUSIONS Few studies have investigated in-depth perspectives of older adults with dementia and their caregivers following direct interaction with an assistive prompting robot. To fulfill the potential of robots, continued dialogue between users and developers, and consideration of robot design and caregiving relationship factors are necessary.

Speech Interaction with Personal Assistive Robots Supporting Aging at Home for Individuals with Alzheimer’s Disease

Increases in the prevalence of dementia and Alzheimer’s disease (AD) are a growing challenge in many nations where healthcare infrastructures are ill-prepared for the upcoming demand for personal caregiving. To help individuals with AD live at home for longer, we are developing a mobile robot, called ED, intended to assist with activities of daily living through visual monitoring and verbal prompts in cases of difficulty. In a series of experiments, we study speech-based interactions between ED and each of 10 older adults with AD as the latter complete daily tasks in a simulated home environment. Traditional automatic speech recognition is evaluated in this environment, along with rates of verbal behaviors that indicate confusion or trouble with the conversation. Analysis reveals that speech recognition remains a challenge in this setup, especially during household tasks with individuals with AD. Across the verbal behaviors that indicate confusion, older adults with AD are very likely to simply ignore the robot, which accounts for over 40% of all such behaviors when interacting with the robot. This work provides a baseline assessment of the types of technical and communicative challenges that will need to be overcome for robots to be used effectively in the home for speech-based assistance with daily living.

Measuring the Efficacy of Robots in Autism Therapy: How Informative are Standard HRI Metrics'

A significant amount of robotics research over the past decade has shown that many children with autism spectrum disorders (ASD) have a strong interest in robots and robot toys, concluding that robots are potential tools for the therapy of individuals with ASD. However, clinicians, who have the authority to approve robots in ASD therapy, are not convinced about the potential of robots. One major reason is that the research in this domain does not have a strong focus on the efficacy of robots. Robots in ASD therapy are end-user oriented technologies, the success of which depends on their demonstrated efficacy in real settings. This paper focuses on measuring the efficacy of robots in ASD therapy and, based on the data from a feasibility study, shows that the human-robot interaction (HRI) metrics commonly used in this research domain might not be sufficient. Categories and Subject Descriptors H.3.4 [Systems and Software]: [Performance evaluation (efficiency and effectiveness)]; J.4 [Social and Behavioral Sciences]: [Psychology]

Analysis of reactions towards failures and recovery strategies for autonomous robots

Human-robot interaction involving the failure of autonomous robots is not yet well understood. We conducted two online surveys with a total of 1200 participants in which people assessed situations where an autonomous robot experienced different kinds of failure. This information was used to construct a measurement scale of peoples reaction to failure where positive values correspond with increasingly positive reactions and negative values with negative reactions. We then used this scale to compare different kinds of failure situations, including the severity of the failures, the context risk involved, and the effectiveness of different kinds of recovery strategies. We found evidence that the effectiveness of recovery strategies depends on the task, context, and severity of failure.

Speech recognition in Alzheimer's disease with personal assistive robots

To help individuals with Alzheimer’s disease live at home for longer, we are developing a mobile robotic platform, called ED, intended to be used as a personal caregiver to help with the performance of activities of daily living. In a series of experiments, we study speech-based interactions between each of 10 older adults with Alzheimers disease and ED as the former makes tea in a simulated home environment. Analysis reveals that speech recognition remains a challenge for this recording environment, with word-level accuracies between 5.8% and 19.2% during household tasks with individuals with Alzheimer’s disease. This work provides a baseline assessment for the types of technical and communicative challenges that will need to be overcome in human-robot interaction for this population.

Deep recurrent Q-learning of behavioral intervention delivery by a robot from demonstration data

We present a learning from demonstration (LfD) framework that uses a deep recurrent Q-network (DRQN) to learn how to deliver a behavioral intervention (BI) from demonstrations performed by a human. The trained DRQN enables a robot to deliver a similar BI in an autonomous manner. BIs are highly structured procedures wherein children with developmental delays/disorders (e.g. autism, ADHD, etc.) are trained to perform new behaviors and life-skills. Mounting anecdotal evidence from human-robot interaction (HRI) research has shown that BI benefits from the use of robots as a delivery tool. Most of the HRI research on robot-based intervention relies on tele-operated robots. However, the need for autonomy has become increasingly evident, especially when it comes to the real-world deployment of these systems. The few studies that have used autonomy in robot-based BI relied on hand-picked features of the environment in order to trigger correct robot actions. Additionally, none of these automated architectures attempted to learn the BI from human demonstrations, though this appears to be the most natural way of learning. This paper represents the first attempt to design a robot that uses LfD to learn BI. We generate a model then correctly predict appropriate actions with greater than 80% accuracy. To the best of our knowledge, this is the first attempt to employ DRQN within an LfD framework to learn high level reasoning embedded in human actions and behaviors simply from observations.

A learning from demonstration framework to promote home-based neuromotor rehabilitation

The paper proposes a learning from demonstration (LfD) framework which will enable children with motor disabilities to perform neuromotor rehabilitation exercises at home- and community- settings. LfD, a popular robot learning paradigm, has traditionally been used to teach embodied robots different skills through demonstrations by lay users. In this paper, we propose a novel application of LfD in the health-care domain. The goal of the proposed LfD framework is to learn standard rehabilitation exercises from a therapists demonstration during a patients clinic visit and assist the patient to perform the exercises at home through demonstrating (using a 3D avatar) different steps of the exercise. Motion information and EMG signals of a patient are used to train a Markov Decision Process (MDP) model with different steps of the exercise from real-time demonstrations. The MDP model then tracks the progress of a patient as (s)he performs the exercise at home and provides prompts if there is any error or missed steps. The MDP model also allows quantitative evaluation of a patients performance and improvements over time, a highly desirable property of any home-based rehabilitation system.

Augmented Reality Eyeglasses for Promoting Home-Based Rehabilitation for Children with Cerebral Palsy

We have designed an augmented reality (AR) game for children with cerebral palsy (CP) to perform home-based neurorehabilitation. A Myo armband detects electromyographic (EMG) signals and accelerometer data from the arm, and a trained classifier determines whether the neuromotor performance of the arm satisfies the expectation of the exercise. The user can move a virtual object only through therapist-prescribed motor movement. The user completes the exercise by moving the virtual object to some targets displayed in the glass.