Intuitive Human-Machine Interfaces for Non-Anthropomorphic Robotic Hands

As robots become more prevalent in our everyday lives, both in our workplaces and in our homes, it becomes increasingly likely that people who are not experts in robotics will be asked to interface with robotic devices. It is therefore important to develop robotic controls that are intuitive and easy for novices to use. Robotic hands, in particular, are very useful, but their high dimensionality makes creating intuitive human-machine interfaces for them complex. In this dissertation, we study the control of non-anthropomorphic robotic hands by non-roboticists in two contexts: collaborative manipulation and assistive robotics. In the field of collaborative manipulation, the human and the robot work side by side as independent agents. Teleoperation allows the human to assist the robot when autonomous grasping is not able to deal sufficiently well with corner cases or cannot operate fast enough. Using the teleoperator’s hand as an input device can provide an intuitive control method, but finding a mapping between a human hand and a non-anthropomorphic robot hand can be difficult, due to the hands’ dissimilar kinematics. In this dissertation, we seek to create a mapping between the human hand and a fully actuated, non-anthropomorphic robot hand that is intuitive enough to enable effective real-time teleoperation, even for novice users. We propose a low-dimensional and continuous teleoperation subspace which can be used as an intermediary for mapping between different hand pose spaces. We first propose the general concept of the subspace, its properties and the variables needed to map from the human hand to a robot hand. We then propose three ways to populate the teleoperation subspace mapping. Two of our mappings use a dataglove to harvest information about the user's hand. We define the mapping between joint space and teleoperation subspace with an empirical definition, which requires a person to define hand motions in an intuitive, hand-specific way, and with an algorithmic definition, which is kinematically independent, and uses objects to define the subspace. Our third mapping for the teleoperation subspace uses forearm electromyography (EMG) as a control input. Assistive orthotics is another area of robotics where human-machine interfaces are critical, since, in this field, the robot is attached to the hand of the human user. In this case, the goal is for the robot to assist the human with movements they would not otherwise be able to achieve. Orthotics can improve the quality of life of people who do not have full use of their hands. Human-machine interfaces for assistive hand orthotics that use EMG signals from the affected forearm as input are intuitive and repeated use can strengthen the muscles of the user's affected arm. In this dissertation, we seek to create an EMG based control for an orthotic device used by people who have had a stroke. We would like our control to enable functional motions when used in conjunction with a orthosis and to be robust to changes in the input signal. We propose a control for a wearable hand orthosis which uses an easy to don, commodity forearm EMG band. We develop an supervised algorithm to detect a user’s intent to open and close their hand, and pair this algorithm with a training protocol which makes our intent detection robust to changes in the input signal. We show that this algorithm, when used in conjunction with an orthosis over several weeks, can improve distal function in users. Additionally, we propose two semi-supervised intent detection algorithms designed to keep our control robust to changes in the input data while reducing the length and frequency of our training protocol

Mechanical Redesign and Implementation of Intuitive User Input Methods for a Hand Exoskeleton Informed by User Studies on Individuals with Chronic Upper Limb Impairments

Individuals with upper limb motor deficits due to neurological conditions, such as stroke and traumatic brain injury, may exhibit hypertonia and spasticity, which makes it difficult for these individuals to open their hand. The Hand Orthosis with Powered Extension (HOPE) Hand was created in 2018. The performance of the HOPE Hand was evaluated by conducting a Box and Blocks test with an impaired subject. Improvements were identified and the HOPE Hand was mechanically redesigned to increase the functionality in performing grasps. The original motor configuration was reorganized to include active thumb flexion and extension, as well as thumb abduction/adduction. An Electromyography (EMG) study was conducted on 19 individuals (10 healthy, 9 impaired) to evaluate the viability of EMG device control for the specified user group. EMG control, voice control, and manual control were implemented with the HOPE Hand 2.0 and the exoskeleton system was tested for usability during a second Box and Blocks test

Advancing the Functionality and Wearability of Robotic Hand Orthoses Towards Activities of Daily Living in Stroke Patients

Post stroke rehabilitation is effective when a large number of motor repetitions are provided to patients. However, conventional physical therapy or traditional desktop-size robot aided rehabilitation do not provide sufficient number of repetitions due to cost and logistical barriers. Our vision is to realize a wearable and functional hand orthosis that could be used outside of controlled, clinical settings, thus allowing for more training repetitions. Furthermore, if such a device can prove effective for Activities of Daily Living (ADLs) while actively worn, this can incentivize patients to increase its use, further enhancing rehabilitative effects. However, in order to provide such clinical benefits, the device must be completely wearable without obtrusive features, and intuitive to control even for non-experts. In this thesis, we thus focus on wearability, functionality, and intuitive intent detection technology for a novel hand robot, and assess its performance when used both as a rehabilitative device and an assistive tool. A fully wearable device must deliver meaningful manipulation capability in small and lightweight package. In this context, we investigate the capability of single-actuator devices to assist whole hand movement patterns through a network of exotendons. Our prototypes combine a single linear actuator (mounted on a forearm splint) with a network of exotendons (routed on the surface of a soft glove). We investigate two possible tendon network configurations: one that produces full finger extension (overcoming flexor spasticity) and one that combines proximal flexion with distal extension at each finger. In experiments with stroke survivors, we measure the force levels needed to overcome various levels of spasticity and to open the hand for grasping using the first of these configurations, and qualitatively demonstrate the ability to execute fingertip grasps using the second. Our results support the feasibility of developing future wearable devices able to assist a range of manipulation tasks. In order to further improve the wearability of the device, we propose two designs that provide effective force transmission by increasing moment arms around finger joints. We evaluate the designs with geometric models and experiment using a 3D-printed artificial finger to find force and joint angle characteristics of the suggested structures. We also perform clinical tests with stroke patients to demonstrate the feasibility of the designs. The testing supports the hypothesis that the proposed designs efficiently elicit extension of the digits in patients with spasticity as compared to existing baselines. With the suggested transmission designs, the device can deliver sufficient extension force even when the users have increased muscle tone due to fatigue. The vision of an orthotic device used for ADLs can only be realized if the patients are able to operate the device themselves. However, the field is generally lacking effective methods by which the user can operate the device: such controls must be effective, intuitive, and robust to the wide range of possible impairment patterns. The variety of encountered upper limb impairment patterns in stroke patients means that a single sensing modality, such as electromyography, might not be sufficient to enable controls for a broad range of users. To address this significant gap, we introduce a multimodal sensing and interaction paradigm for an active hand orthosis. In our proof-of-concept implementation, EMG is complemented by other sensing modalities, such as finger bend and contact pressure sensors. We propose multimodal interaction methods that utilize this sensory data as input, and show they can enable tasks for stroke survivors who exhibit different impairment patterns. We then assess the performance of the robotic orthosis for two possible roles: as a therapeutic tool that facilitates device mediated hand exercises to recover neuromuscular function, or as an assistive device for use in everyday activities to aid functional use of the hand. 11 chronic stroke (> 2 years) patients with moderate muscle tone (Modified Ashworth Scale ≤ 2 in upper extremity) engage in a month-long training protocol using the orthosis. Individuals are evaluated using standardized outcome measures, both with and without orthosis assistance. The results highlight the potential for wearable and user-driven robotic hand orthoses to extend the use and training of the affected upper limb after stroke. The advances proposed in this thesis have the potential to enable robotic based hand rehabilitation during daily activities (as opposed to isolated hand exercises with limited upper limb engagement) and over extended periods of time, even in a patient’s home environment. Numerous challenges must still be overcome in order to achieve this vision, related to design (compact devices with easier donning/doffing), control (robust yet intuitive intent inferral), and effectiveness (improved functionality in a wider range of metrics). However, if these challenges can be addressed, wearable robotic devices have the potential to greatly extend the use and training of the affected upper limb after stroke, and help improve the quality of life for a large patient population

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

A review of the effectiveness of lower limb orthoses used in cerebral palsy

To produce this review, a systematic literature search was conducted for relevant articles published in the period between the date of the previous ISPO consensus conference report on cerebral palsy (1994) and April 2008. The search terms were 'cerebral and pals* (palsy, palsies), 'hemiplegia', 'diplegia', 'orthos*' (orthoses, orthosis) orthot* (orthotic, orthotics), brace or AFO