Future trends in wearable assistive robots

Wearable assistive robots

Wearable assistive robots, which are designed to assist humans with physical impairments, particularly assisting lower and upper limbs, and joints of the human body, could provide users with brand-new platforms and methods for assistance, care, and rehabilitation.

Given the developments in areas like sensor technology, fabrication materials, machine learning and control techniques, and computational power in recent years, wearable assistive robotics has made impressive strides.

However, there are still areas that need to be taken into account and improved for the future development of wearable assistive robotics to achieve the goal of having intelligent and comfortable wearable systems that can help humans perform daily activities independently in a natural, natural, safe, and efficient. These areas include materials and sensing, learning and adaptability, datasets, and standards.

In this post, we will forecast some of the key future trends in wearable assistive robots.

1. Hybrid assistive systems

Currently, rigid or soft materials are used to construct most assistive robots. Systems that can aid humans in performing daily tasks and enhancing their strength to perform tasks in the industry have been developed using rigid materials on a large scale. Since soft materials are lightweight, flexible, and do not restrict the human body’s natural movement, typically with rigid materials, they have attracted more attention in recent years for the development of wearable robots. Despite these traits, they cannot offer the necessary torque to help a human, such as lifting the legs for locomotion activities.

A better trade-off for the design of assistive robots can be found in hybrid approaches that combine rigid and soft components. Using this method, the robot structure can be lightweight and flexible enough to conform to the human body while safely delivering the necessary torque. This kind of robot design improves the sensing modalities and methods that can be integrated within the hybrid structure. As a result, there may be more opportunities for research into cutting-edge machine learning and control techniques that can learn how much and what kind of assistance is needed. We anticipate that the development of wearable assistive robots will heavily rely on hybrid approaches embedded with a wide range of sensing technologies.

2. Learning and Adaptability

The ability to learn from users and gradually adapt to their needs are two essential elements for creating robots that can safely and effectively assist humans. Current wearable assistive robots are made to help people in specific, controlled situations, like helping people walk, sit up and stand, or reach out and grab something in a lab setting. These systems frequently malfunction when tested outdoors or when test conditions are slightly altered. It is crucial to research and develop techniques that enable the creation of robots that can learn from their users and adjust to changes in their environment.

The development of safe and dependable assistive systems, as seen in other robotic applications, can be approached using advanced intelligent robot architectures for data processing at various levels of abstraction. These architectures typically include memory modules, reactive and adaptive layers, and modules for sensing, data fusion, perception, decision-making, and control, as well as ongoing learning and adaptive processes. This strategy can be used, for instance, in wearable assistive robot architectures along with cutting-edge machine learning and control techniques to enable the robot to recognize the activity being performed by the human and provide the necessary assistance.

Future assistive systems must be designed in the context of the task or activity to achieve adaptability. The reliability of decisions and assistive actions taken by the robot is increased by wearable robots that, for instance, can determine whether a person is at home or work. This enables the system to identify the most likely actions that the person would perform. With this strategy, it may be possible to create intelligent wearable assistive robots that can not only adapt to various changing environmental conditions but also safely respond to unseen or unexpected events or data.

3. Datasets and Standards

Significant advancements in robotics over the past few decades have the potential to greatly benefit society; however, they must be properly regulated and ethically developed. Two important pillars in the ethical development of new robots are datasets and standards. At the moment, significant research is conducted solely due to the availability of datasets rather than a significant unmet clinical need (there is often a poor correlation between clinical need and dataset availability). There are numerous publicly accessible datasets for ADL; however, analysis and replication are difficult because they are frequently gathered and prepared using various protocols. Standards for thorough and dependable data collection, preparation, and matching to clinical needs are essential for allowing researchers to replicate the data collection and analysis. The implementation of standards and regulations must be closely related to emerging ethics.