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Department of Computer Science

Next Generation Smart Health Frameworks to support Disabled Individuals and Rehab patients

 

We are exploring innovative smart health related cyber-physical frameworks that involve Virtual/Mixed Reality environments to help (a) Disabled individuals (in their workplace and at home) and (b) Rehabilitation patients.

As part of a pilot project, we are working with doctors, nurses and therapists and exploring designing cyber-physical framework for both disabled and rehab patients.

 

There are several benefits to creating such smart health frameworks

  • They enable more effective and user-friendly exposure to the therapy procedures (and steps)
  • They are available (24/7) ‘on demand’
  • They reduce the need for face-to-face contact between caregivers and patients (and can serve as safe mediums between therapists and patients especially during a pandemic)
  • They can support IoT based cyber-physical interactions that also will provide supervised assistance by rehab therapists with patients in various remote or distant locations.

Using a Mixed Reality (MR) headset, a patient can be guided by a therapy ‘avatar’ inside the 3D environment which is displayed on the headset (which the patient can watch, see image) and then repeat the therapy steps in the real or physical world. Using a MR approach, a patient can interact with the cyber (3D/virtual) world as well as the physical or real world. The image below illustrates this approach.

 

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A next generation cyber-physical approach involving Mixed Reality mediums for Rehab therapy

 

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Step2

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Views of a three step therapy training session ( which is projected onto the MR headset worn by the patient)

 

Helping patients who suffer a stroke or lose a limb

There are 2 contexts for helping individuals who suffer a stroke or lose a limb; one is at home and the other is in the workplace.

Home Contexts

When they have to wear a prosthetic arm (after a limb removal or due to a stroke) there is a need to assess if a prosthetic arm is a good fit for them to help them do various tasks (in their homes or at their workplace). With VR based approaches, a patient can virtually ‘wear’ different prosthetic devices which will allow them to perform various tasks and then decide which is the best choice for them.  For example, they can perform various tasks within the (3D) digital twin of their home and assess picking up a can from a shelf, opening a jar, etc. Such a virtual evaluation can be performed prior to purchasing a given prosthetic and it can be used to compare various prosthetic devices before selecting the one best suited for a given patient. Both VR and MR models have a role to play in such a practice. MR based training can help disabled patients to become acclimatized or familiar with their work area and tasks; our research is also studying the impact of such approaches on the cognition aspects of individuals; assessing the effect of such environments on cognition (comprehension, concentration, awareness) will throw more light on the HCI related aspects which are important as our world embraces the metaverse related technologies.

 

Work or Factory Contexts

In a factory or manufacturing workplace context, a disabled patient can be better prepared for their work tasks by virtually performing various tasks (such as loading a part onto an robotic assembly machine, etc.). By virtually wearing a prosthetic arm (figure 1 b), they can practice performing various tasks at home or offline before getting back to the workplace. Equally important, such a 3D virtual assessment can enable factory administration to also install needed safety mechanisms or make modifications to ensure the disabled individual can be perform their responsibilities in a safe and productive manner.

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Training and Assesment of Prosthetic devices for disabled individuals in factory or work contexts

 

Virtual Training and Assessment of Prosthetic limbs for disabled workers in a factory context

As part of a pilot, we are  designing a preliminary cyber-physical framework including Motion based planning using 3D models to enable individuals with limited mobility and dexterity to accomplish various tasks at home and in a factory context.

 

Tracking sensors and cameras can provide real-time data which can be used to identify obstacles and help plan collision free paths. Further, stroke patients may need assistance cognitively to help accomplish various tasks using software tools. Such cyber-physical interactions and frameworks hold the potential to assist visually impaired and mobility restricted patients in a more patient friendly and effective manner.

 

With the advent of Industry 4.0, the potential to help disabled patients including those who have suffered a stroke has increased.  Cyber-physical tools including VR/MR environments have a key role to play in the design of novel approaches to realize this potential.

 

Role of cognitively enhanced 3D digital twins to support training of disabled workers in the context of Cyber-Human-Physical Frameworks in the Factories of the Future

 

The emergence of 3D digital twins-based approaches holds significant potential in addressing issues of collaboration and training in Manufacturing and other engineering contexts. Such a 3D twin of a manufacturing factory can be modeled at various levels of abstraction, beginning with a machine, a work cell, or the entire factory. It seeks to provide a realistic simulation environment that can serve as a link to the cyber and physical worlds. At the same time, there are two other thrusts where the role of such 3D twins assumes significance. The first involves cognitive-related training of humans in collaborative human-machine interactions on the shop floor. The second is its potential to study the feasibility of humans (who have undergone rehabilitation or are  disabled) functioning in simple or complex manufacturing environments.

 

Interoperability can be described as the ability of computer systems or software to exchange and use information. The challenges become more complex in a cyber-physical context as the data/information exchange needs to occur between cyber-physical entities. In many manufacturing contexts, humans (or human workers) play a vital role in manufacturing and assembly activities, termed 'human-in-the-loop' (HITL) operations. Such systems can be viewed as cyber-human-physical systems (CHPS). While data/information interoperability still remains the overall objective, the ability of humans to adapt to changes in cyber-physical interactions is an important issue that needs to be addressed.  

 

The role of 3D digital twins is to propose, compare, assess, and validate  alternative Manufacturing plans in such cyber-human-physical (CHP) interactions assume significance. In the context of Human in the Loop (HITL) manufacturing operations, although there may be a high level of automation, including the exchange of data/information's role in achieving this data/information interoperability needs to be understood and studied.  The availability ability and make of the prod/process information depends not only on the data/information format (or standards, if any) but also on the cognitive capabilities of the humans involved in HITL operations.  In such contexts, the training and the preparation of humans engaged in manufacturing operations becomes significant.  There are two broader categories of contexts: Training and cognitive responses of humans in (a) a new (or planned) manufacturing environment or factory and (b) a modified factory or environment where changes are planned to be implemented. For both these categories of contexts, 3D-based digital twins can be designed, validated, and used to support training and throw light on humans' performances in manufacturing environments.

 

An overlooked aspect is the adaptability of humans in such CHP contexts. 3D digital twins can be viewed as simulation environments used to model an existing CHP environment in a manufacturing context or to study proposed alternatives or changes to a current CHP environment. A significant issue is the study of cognitive problems related to humans working in such environments. The potential of such 3D twins to train disabled workers and others who have undergone rehab treatment, including being fitted with a prosthetic arm is high. In this paper, we discuss the design of such cognitive-intensive 3D digital twins to support training of disabled workers to function in Human in the Loop (HITL) manufacturing contexts. We also discuss the role of such 3D twins in facilitating training and provide a simulation context to asses the cognitive elements of such CHP systems and environments.

 

The design constructs in this paper of such digital twins and CHP framework elements are outcomes of a pilot project targeting the training of disabled and rehabilitation patients returning to the workplace. The role of such 3D digital twins supported by Virtual Reality and Mixed Reality (VR/MR)environment has significant potential in such factories of the future. The specific cognitive elements in this framework seek to illuminate the cognitive responses of humans involved in such CHP environments.  The impact of 3D digital twin-based environments on comprehension, knowledge acquisition, awareness, and concentration attributes can be studied to enrich the digital training environments.  Subsequently, the efficiency and effectiveness of such complex CHP environments can be improved through next-generation VR/MR environments.

 

Such 3D digital twins have a vital role in realizing next-generation future factories, which can be based on Industry 4.0 principles. The emphasis on cognitive elements, especially in HITL manufacturing environments, is essential to ensure that the functioning of humans with advanced software modules, IoT sensors, and other cyber-physical resources is successful. Such success is necessary for the realization of interoperability objectives. Without the harmonizing involvement of the human HITL operations, the full benefits of interoperability cannot be achieved.

 

The manufacturing context for the discussion in this paper involves HITL, working closely with collaborative robots (or cobots) and other manufacturing resources (such as conveyors, cameras/sensors, other sensors). A brief discussion of cobots working with disabled individuals as well as VR based approaches to assist visually impaired individuals is discussed. In a comprehensive review of assistive technologies, Thiyagarajan et al. (2019) discussed  intelligent guide robots designed to enhance navigation for individuals with visual impairments. The paper surveys various robotic systems and strategies to improve accessibility and safe mobility for the visually impaired [6]. Neto et al. (2015) discussed developing innovative, competent, sophisticated, sophisticated automated mobility aids equipped with sensors and intelligent control systems. These devices offer enhanced support for individuals with mobility challenges, including those with visual impairments, making them a promising tool to improve independent mobility and safety [7]. 

 

In [8], the focus is on discussing the move from automation to human-robot collaboration in production and the significance of efficient control systems, including sensors and software, for safety and productivity. It also examines how human observation affects robot behaviors and addresses ergonomic evaluations and EMG-based exoskeletons to improve collaborative robot design and performance. In [9], the authors discussed Cobot applications in industry and services, emphasizing control system safety and efficiency. Data-driven interface optimization, physical cooperation (contact detection, force estimate), and virtual impedance control for Manufacturing and industrial collaborative robot movements are also discussed. Other papers explore contemporary COBOT applications, emphasizing safety and efficiency control systems in human-shared settings. Ergonomics, EMG-based exoskeletons, data-driven interface, and physical cooperation are examined [10]. In [11, 12], cobots are proposed as factory and service sector collaborators, notably in care and treatment. The article discusses care environments' pros and cons, human-robot interaction, and testing in nursing and retirement homes, highlighting the necessity for functionality and acceptability.

 

The CHP oriented frameworks have the potential to support the adoption of key principles underlying Industry 4.0 [14-19].  The role of 3D Virtual/Mixed Reality based environments and technologies can enhance the design and implementation of such CHP frameworks especially when considering contexts involving disabled humans working with cobots in advanced manufacturing environments. The training of such disabled workers need to address cognitive aspects which play a key role in the effective design of such HITL based manufacturing systems.

 

The digital twin based design of chp systems in manufacturing

 

A disabled worker may be fitted with a prosthetic hand, or a patient who has completed rehab treatment may be equipped with a prosthetic device to help them pick up objects, turn on a machine or do other tasks working closely with cobots.  Before being introduced to such a HITL factory environment, such disabled workers (or any worker) can be trained, and the performance is assessed in the context of where Manufacturing is, as well as cognitive objectives. The cognitive goals can involve the following

1. Can the disabled worker perform a target set of manufacturing activities in that specific factory setting where there can be potential audio and visual distractions that can affect the cognitive abilities and increase the cognitive load on the human worker? 

 

2. Can the disabled worker who has a prosthetic device attached to their arm or hand be physically capable of performing a target set of physical operations working with the Cobalt as well as conveyors or other automated or semi-automated machines in an advanced manufacturing setting

 

There are 2 thrusts of importance: (a) the role of digital twins to support the analysis of cognitive responses in complex HITL contexts (b) Study of physical capabilities involving disabled workers wearing prosthetic devices. Two types of digital twins can be designed and used to train and assess a particular set of physical capabilities when a disabled worker is fitted with a prosthetic device. Before the simulation-based analysis, the form and fit of the 3D prosthetic device can be virtually studied and analyzed for compatibility with the human arm or wrist of the disabled worker who is expected to work with cobots with the manufacturing cell. Several elements can be studied virtually in such a 3D digital twin based simulation environment

Type 1: Analysis of the reachability of the target object or device by the worker equipped with the prosthetic device.

The reachability of a disabled worker equipped with a prosthetic device can be defined as its ability to reach the targe object moving the arms and limbs along with the prosthetic device attached. The reachability of a disabled worker can be assessed ad measured in 3D space inside a 3D space to reach given target. The target could be a three-dimensional object on the conveyor, picking up a target object or assembling one object on top of another, etc..

 

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Fig 1: Studying the feasibility of reachability when interacting with a cobot in a manufacturing work cell

 

Type 2: Analysis of dexterous manipulation capabilities that the prosthetic device facilitates the disabled worker to perform,

 

The dexterous manipulation capability is also a function of the graspability of the prosthetic device, which is essential to complete the task of picking up an object, transporting it to a different location, and turning on or turning off a sensor in an advanced manufacturing work cell.

 

 3D digital twins can be designed and built using virtual reality or mixed reality platforms; subsequently, the disabled worker can be trained with the help of such 3D digital twins, which can also be used for assessing the cognitive responses as well as the feasibility of the prosthetic device to facilitate a disabled worker to perform or assist in the accomplishment of specific manufacturing tasks. In a virtual reality environment or platform, during training, the disabled worker can wear the headset, interact with the controllers and complete the needed training. As indicated earlier, two thrusts need to be recognized. The first refers to understand the impact of such 3D twins on the human worker’s cognitive attributes including comprehension and understanding of the target processes and their complexities (this is closely related to the acquisition of knowledge of an existing or modified process) as well as awareness and concentration during the collaborative HITL activities.

The response of the disabled worker in terms of distractions will affect concentration as well as awareness, which will play a key role in not only accomplishing complex manufacturing activities but also help such workers be mentally prepared to take immediate actions to ensure their own safety in the case of unexpected or unplanned movement of a robot, cobot or failure of a machine tool which may lead to unsafe situation. The level of preparation and training can be assessed using such 3D digital twins. In the preliminary stages, there is a need to assess the impact of the planned activities in conjunction with the manufacturing operations. The creation of a VR or Mixed Reality based simulation environment will enable such a virtual assessment of a planned manufacturing system (which can either be a new system created from ground zero or it can be a modified manufacturing system). Fig 3 shows a digital twin created using the Vive VR platform.  This environment supports the process engineers to consider various process flow alternatives involving the cyber-human and manufacturing resources. This 3D digital twin also enables adoption of concurrent engineering principles as it allows cross functional teams of engineers (designers, process or manufacturing specialists, testing engineers, customers as well as workers) to discuss various alternatives. The process simulation aspects also enables the comparison of various cycle and assembly cycle times for various layouts and configurations. Fig 2 shows a slightly modified layouts with the same general flow involving different cobots in different positions.

 

Manufacturing layout 1

Fig 2: Manufacturing layout 1

Manufacturing layout 2

Fig 3: Manufacturing layout 2

 

Consider the manufacturing layout shown in Figure 3. The worker needs to be able to concentrate on the manufacturing or assembly tasks in progress by the Cobot with whom they interact as part of the manufacturing operations. At the same time, this worker needs to possess a certain level of awareness to ensure that they react quickly when a robot goes out of control, or something else occurs in the periphery of their field of view. In a 3D environment, this can be created by simulating different scenarios, such as a robot moving in an unplanned path or another robot from a neighboring work cell entering the human-robot collaborative workspace near the disabled worker, making it unsafe and requiring the disabled worker to respond promptly. Such cognitive assessments are needed to validate as well as modify any proposed layout or process flows (due to outcomes of the virtual analysis using the 3D twins). Other cognitive assessment can be used to measure the comprehension and understanding of the process intricacies which can be assessed with the creation of 3D challenge scenarios which probe the level of understanding of the target manufacturing process by the disabled or other workers.

 

Assessment scenarios can study the time taken to respond, the level of preparation or training and the cognitive load of the disabled workers when these distractions, disturbances, or accidents occur; these outcomes can be used to either redesign the manufacturing environment or increase the level of preparation of the disabled worker wearing the prosthetic device. 

 

The assessment of the physical capabilities of a disabled worker with a prosthetic device can involve the creation of a digital twin with both audio and avatar based cues/instructions during training, which can help the disabled worker be trained to complete all the necessary target activities using a controller interface. Prior research [Gupta 2021] has demonstrated that during training, when an individual observes an avatar of themselves performing specific tasks, their understanding of a given task increase. Adopting that principle, a 3D digital twin can provide a detailed simulation of  a disabled worker avatar (equipped with a prosthetic device, see fig 1), with specific reachability and dexterous manipulation capabilities which may be necessary to complete a given set of manufacturing operations. Based on this simulation based training and assessment outcomes, there are at least two options available: (a) if it appears that the disabled worker is unable to perform the tasks in an Cobot-based CHP environment, because of reachability or cognitive overload reasons, then it may be necessary to redesign the CHP environment, or (b) it may be necessary to assign the disabled worker to a different manufacturing operation that they are capable of performing.

 

The capabilities of various prosthetic devices which can be fitted to the disabled workers returning to the factory can be virtually compared and analyzed at various elements of detail or abstraction. The 3D digital twins can be used to assess reachability as well as study the assembly capabilities needed for a given set of assembly or manufacturing tasks. By modeling the grasping and manipulation degrees of freedom and simulating (or mimicking) the grasping aspects of the prosthetic hand or device, a virtual analysis of the disabled workers capabilities and task completion can be accomplished; subsequently, changes to the manufacturing layout or configuration or process details can be proposed, compared and analyzed.

 

Virtual Reality and Mixed Reality-based 3D digital twins

 

While a virtual reality-based environment is less expensive than a mixed reality training environment, there are some benefits that a mixed reality-based 3D digital environment can provide the human worker during training. When interacting with a digital twin with a purely immersive 3D environment (such as a Vive platform), the disabled worker’s reference to the real world is eliminated, and they can interact only with a simulated 3D environment of the target manufacturing environment. The training environment can include a training Avatar that introduces and explains the various user interface capabilities as guides the worker through a given set of training steps and operations. The assessment of cognition can be accomplished through the creation of 3D challenge scenarios where the worker has to perform various tasks working with the cobot which are modifications to the general flow of operations they have been trained on. This allows for cognitive assessment of memory, comprehension, awareness and concentration.

 

Fig 4. MR Headset

Fig 4. MR Headset

 

The mixed reality (MR) environment has a certain level of engagement that may be beneficial in specific contexts. A Mixed Reality headset (fig 4, allows a user to see the simulation scene on the headset display as well as see/interact with the real or physical world or manufacturing environment. For example, during training, they can watch the various instructions along with the 3D layout along with audio and visual cues as well as can see a worker avatar performing various assembly or other steps virtually. While training can be completed in an immersive VR environment, after training, when the disabled worker or operator is in the actual manufacturing setting, they may choose to wear a mixed-reality headset where their actions can be guided with the help of audio or avatar-based cues from the 3D headset they are wearing. The impact of such mixed reality environments on the cognitive abilities of humans has been studied as part of other research efforts [Gupta papers]. However, additional research is needed to better understand the impact of such mixed reality environments on the cognitive load and responses of humans involved in such collaborative manufacturing activities.

 

ANALYSIS OF COGNITIVE ELEMENTS IN 3D DIGITAL TWINS

Cognitive assessments are very important for designing and improving virtual settings for different 

uses. These tests help assess the cognitive responses of workers as well as help measure if cognitive overload occurs during such complex cyber-human-physical activities. The design of the CHP frameworks involves the field of HCI, which stands for Human-Computer Interaction. HCI is a field that looks at how people and computers interact, intending to make systems that are efficient and easy to use [1]. HCI oriented frameworks for manufacturing typically involve interdisciplinary perspectives including manufacturing engineering, psychology, human factors and computer science. 

 

Some additional terms need to be defined to better understand the design of such cognitive enhanced CHP frameworks and the role of digital twins in such a framework. Cognition is the neural process of taking in, processing, and using knowledge. It includes attributes such as  perception, memory, problem-solving, and making decisions [NEED REF HERE]. Understanding the role of cognitive attributes is essential for designing CHP environments for manufacturing and other fields.  Based on this understanding, more effective HITL and CHP environment for manufacturing can be designed and implemented.

Cognitive load is the cerebral effort taken to understand and perform various tasks [3]. Managing and reducing such cognitive load is essential to designing 3D digital twins so that workers can be trained to perform at their best while not getting overwhelmed (or cognitively overloaded).

 

Concentration and awareness are two different mental states. Concentration is paying attention to one task (that is the focus of a workers mind) while blocking out other elements in a given scenario [4]. On the other hand, awareness [5]can be described as broader state of being aware of one's surroundings including intrusions into the surrounding, distractions (audio or visual) including movement which can be benign or unsafe in manufacturing contexts. In simulated settings, it's essential to balance focusing on tasks and being aware of what's happening around them. There needs to be a balance between awareness and concentration when designing such cognitively enhanced 3D digital twins.

 

Cognitive tests are essential for designing as well as assessing the performance of disabled and other workers in planned or modified manufacturing HITL settings. The adoption of HCI principles is a crucial part of this design process. Understanding how the mind works, figuring out how to handle the cognitive load, and knowing the difference between focus and awareness are  essential principles which need to be taken into consideration during the design of such 3D digital twins.. 

 

design of manufacturing layout alternatives

 

In the pilot design, a simple manufacturing flow was considered involving two work cells, a conveyor, 2 part handling robots and a disabled worker wearing a prosthetic device (fig 3). The digital twins were created using Unity 3D engine and implemented using a fully immersive VR platform (Vive). This provides users with a 3D headset where the training activities are projected and the users can interact with controllers to pick up objects, turn on switches, etc. These digital twins can be designed at various levels of abstraction. For example, they can be created at the work cell level (see fig cc), for a segment of a shop floor or the entire factory. 

 

Fig 5: A view of a 3D digital twin at the work cell level (this is a digital twin of the physical work cell in figure dd)

Fig 5: A view of a 3D digital twin at the work cell level (this is a digital twin of the physical work cell in figure dd)

 

Fig 6: A view of a physical work cell

Fig 6: A view of a physical work cell

 

Overview of a cyber-human-physical framework for manufacturing

Fig  7: Overview of a cyber-human-physical framework for manufacturing

 

Fig 8: closeup of the work cell in fig 66

Fig 8: closeup of the work cell in fig 66

 

Discussion and Conclusion

The proposed 3D digital twin-based cyber-human-physical framework can provide a foundation to design cognitively enriched CHP systems for the manufacturing factories of the future. The effectiveness of data/ information interoperability of such CHP systems in Manufacturing can be enhanced by emphasizing cognitive and functional training of human workers in advanced simulated manufacturing settings. In this context,  3D digital twins play a vital role in designing and implementing such cognitively enhanced advanced cyber-physical systems. These principles align with information centric themes that are emphasized in industry 4.0 (or the Fourth industrial revolution).

 

This paper discussed the preliminary findings of a pilot project that focused on designing 3D digital twins which can facilitate the creation of cognitively enhanced cyber-physical systems for advanced manufacturing. Such 3D digital twins can be used to train disabled workers equipped with prosthetic devices (as well as other workers) to function in an advanced Manufacturing environment involving cobots and other resources.

 

The cyber human physical (CHP) environment considered included two work cells linked by a conveyor-based system and two part-handling robots. The cyber components included assembly planning components, monitoring modules for tracking the progress of the assembly as well as an Enterprise software manager for coordinating the various activities of this complex CHP system. The design elements also focused on emphasizing cognitive elements during training of disabled workers in the HITL activities includes focusing on comprehension of the manufacturing tasks involved as well as measuring concentration and awareness of the disabled worker during the accomplishment of target activities of the complex collaborative manufacturing tasks involving cyber, human and manufacturing components.

 

 

ACKNOWLEDGMENT 

 

The research activities discussed in this paper were funded through grants from the US National Science Foundation  (grant numbers 2050960 and 2106901). Their funding is gratefully acknowledged.

 

References

 

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[2] Anderson, J. R. (2014). Cognitive Psychology and Its Implications. Worth Publishers.

 

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