Motion Capture Sensor-Based Emotion Recognition Using a Bi-Modular Sequential Neural Network

Motion capture sensor-based gait emotion recognition is an emerging sub-domain of human emotion recognition. Its applications span a variety of fields including smart home design, border security, robotics, virtual reality, and gaming. In recent years, several deep learning-based approaches have been successful in solving the Gait Emotion Recognition (GER) problem. However, a vast majority of such methods rely on Deep Neural Networks (DNNs) with a significant number of model parameters, which lead to model overfitting as well as increased inference time. This paper contributes to the domain of knowledge by proposing a new lightweight bi-modular architecture with handcrafted features that is trained using a RMSprop optimizer and stratified data shuffling. The method is highly effective in correctly inferring human emotions from gait, achieving a micro-mean average precision of 0.97 on the Edinburgh Locomotive Mocap Dataset. It outperforms all recent deep-learning methods, while having the lowest inference time of 16.3 milliseconds per gait sample. This research study is beneficial to applications spanning various fields, such as emotionally aware assistive robotics, adaptive therapy and rehabilitation, and surveillance.

Robotics Applications in the Training of Children With Autism Spectrum Disorders

Children with autism spectrum disorders encounter many difficulties in their social interactions with other people. Impairments in verbal and non-verbal communication, as well as repetitive and stereotypical behaviors, are some of the most common characteristics of this disorder. Along with other treatments and methods, the use of robotics is a relatively new and promising field, which can be implemented in most classrooms. Socially assistive robotics (SAR) is a subcategory of robotic technology that emanated from social robotics and assistive robotics. SAR can be used to enhance literacy skills, social communication, adaptive behaviors, and emotion recognition. In this chapter, the authors discuss some of the most popular robotic tools used in autism interventions for school-aged children, as well as ways to implement robotic partners in inclusive settings.

Are Preferences Useful for Better Assistance?

Assistive Robots have an inherent need of adapting to the user they are assisting. This is crucial for the correct development of the task, user safety, and comfort. However, adaptation can be performed in several manners. We believe user preferences are key to this adaptation. In this article, we evaluate the use of preferences for Physically Assistive Robotics tasks in a Human-Robot Interaction user evaluation. Three assistive tasks have been implemented consisting of assisted feeding, shoe-fitting, and jacket dressing, where the robot performs each task in a different manner based on user preferences. We assess the ability of the users to determine which execution of the task used their chosen preferences (if any). The obtained results show that most of the users were able to successfully guess the cases where their preferences were used even when they had not seen the task before. We also observe that their satisfaction with the task increases when the chosen preferences are employed. Finally, we also analyze the user’s opinions regarding assistive tasks and preferences, showing promising expectations as to the benefits of adapting the robot behavior to the user through preferences.

Supporting Older Family Caregivers of Young Adults with IDD: A Pilot Program with Socially Assistive Robotics

Introduction The need for caregiver respite is well-documented for the care of persons with IDD. Social Assistive Robotics (SAR) offer promise in addressing the need for caregiver respite through ‘complementary caregiving’ activities that promote engagement and learning opportunities for a care recipient (CR) with IDD. This study explored the acceptability and usefulness of a SAR caregiver respite program responsive to feedback from both the CRs and their older family caregivers (age 55+). Methods Caregiver-CR dyads (N =11) were recruited. A mixed methods research design was deployed in three phases: Phase I with four focus groups to inform the program design; Phase II for program demonstration and evaluation with pre- and post-surveys; and Phase III with post-program interviews for feedback and suggestions. Results Quantitative data analysis in Phase II showed both caregivers and their CRs scored favorably the social presence of the robot (Pepper) and social engagement with Pepper. Caregivers also reported positive perceptions of Pepper in terms of anthropomorphism, animation, likeability, intelligence, and safety. Content analysis from Phase III interviews suggested that the SAR may offer physical/emotional respite to caregivers by providing companionship/friendship as well as promoting independence, safety/monitoring, and interactive engagement with children. Discussion SAR has potential in providing respite for older family caregiver demonstrated by the levels of CR engagement and learning with Pepper. Future studies need a longer program design and larger sample size to test the feasibility and efficacy of the intervention.

Designing Acceptable Robots for Assisting Older Adults: A Pilot Study on the Willingness to Interact

The aim of this paper was to explore the psychosocial determinants that lead to acceptability and willingness to interact with a service robot, starting with an analysis of older users’ behaviors toward the Robot-Era platform, in order to provide strategies for the promotion of social assistive robotics. A mixed-method approach was used to collect information on acceptability, usability, and human–robot interaction, by analyzing nonverbal behaviors, emotional expressions, and verbal communication. The study involved 35 older adults. Twenty-two were women and thirteen were men, aged 73.8 (±6) years old. Video interaction analysis was conducted to capture the users’ gestures, statements, and expressions. A coded scheme was designed on the basis of the literature in the field. Percentages of time and frequency of the selected events are reported. The statements of the users were collected and analyzed. The results of the behavioral analysis reveal a largely positive attitude, inferred from nonverbal clues and nonverbal emotional expressions. The results highlight the need to provide robotic solutions that respect the tasks they offer to the users It is necessary to give older consumers dedicated training in technological literacy to guarantee proper, long-lasting, and successful use.

Wearable Assistive Robotics: A Perspective on Current Challenges and Future Trends

Wearable assistive robotics is an emerging technology with the potential to assist humans with sensorimotor impairments to perform daily activities. This assistance enables individuals to be physically and socially active, perform activities independently, and recover quality of life. These benefits to society have motivated the study of several robotic approaches, developing systems ranging from rigid to soft robots with single and multimodal sensing, heuristics and machine learning methods, and from manual to autonomous control for assistance of the upper and lower limbs. This type of wearable robotic technology, being in direct contact and interaction with the body, needs to comply with a variety of requirements to make the system and assistance efficient, safe and usable on a daily basis by the individual. This paper presents a brief review of the progress achieved in recent years, the current challenges and trends for the design and deployment of wearable assistive robotics including the clinical and user need, material and sensing technology, machine learning methods for perception and control, adaptability and acceptability, datasets and standards, and translation from lab to the real world.

Living With Assistive Robotics: Exploring the Everyday Use of Exoskeleton for Persons With Spinal Cord Injury

Background: Recent advancements in sensor technology and artificial intelligence mechanisms have led to a rapid increase in research and development of robotic orthoses or “exoskeletons” to support people with mobility problems. The purpose of this case study was to provide insight into the lived reality of using the assistive robotic exoskeleton ReWalk.Method: We used ethnographic techniques to explore the everyday experience and use of the assistive robotic device.Results: We found that the appropriation and integration of the technology within the patient's everyday lives required a social and collaborative effort, which continued into use. The decisions to utilise the technology (or not) was closely tied to physical, social, cultural, environmental, and psychological factors. Consequently, there was much variation in patients' perception of the technology and opportunities for support. Four themes emerged:(a) Meaning of mobility—physical mobility represents more than functional ability. Its present socio-cultural meaning is associated with an individual's self-identity and life priorities.(b) Accomplishing body-technique—integration with the body requires a long process of skill acquisition and re-embodiment.(c) Adaptation and adjustment in use—successful use of the technology was characterised by ongoing adjustment and adaptation of the technology and ways of using it.(d) Human element—introduction and sustained use of the exoskeleton demand a social and collaborative effort across the user's professional and lay resources.Conclusions: This study highlights that the development and implementation of the technology need to be grounded in a deep understanding of the day-to-day lives and experiences of the people that use them.