Wearable haptic systems for the fingertip and the hand: taxonomy, review and perspectives

In the last decade, we have witnessed a drastic change in the form factor of audio and vision technologies, from heavy and grounded machines to lightweight devices that naturally fit our bodies. However, only recently, haptic systems have started to be designed with wearability in mind. The wearability of haptic systems enables novel forms of communication, cooperation, and integration between humans and machines. Wearable haptic interfaces are capable of communicating with the human wearers during their interaction with the environment they share, in a natural and yet private way. This paper presents a taxonomy and review of wearable haptic systems for the fingertip and the hand, focusing on those systems directly addressing wearability challenges. The paper also discusses the main technological and design challenges for the development of wearable haptic interfaces, and it reports on the future perspectives of the field. Finally, the paper includes two tables summarizing the characteristics and features of the most representative wearable haptic systems for the fingertip and the hand

Advances of Italian Machine Design

This 2028 Special Issue presents recent developments and achievements in the field of Mechanism and Machine Science coming from the Italian community with international collaborations and ranging from theoretical contributions to experimental and practical applications. It contains selected contributions that were accepted for presentation at the Second International Conference of IFToMM Italy, IFIT2018, that has been held in Cassino on 29 and 30 November 2018. This IFIT conference is the second event of a series that was established in 2016 by IFToMM Italy in Vicenza. IFIT was established to bring together researchers, industry professionals and students, from the Italian and the international community in an intimate, collegial and stimulating environment

Neuromechanical Model-Based Adaptive Control of Bilateral Ankle Exoskeletons:Biological Joint Torque and Electromyogram Reduction Across Walking Conditions

To enable the broad adoption of wearable robotic exoskeletons in medical and industrial settings, it is crucial they can adaptively support large repertoires of movements. We propose a new human-machine interface to simultaneously drive bilateral ankle exoskeletons during a range of 'unseen' walking conditions and transitions that were not used for establishing the control interface. The proposed approach used person-specific neuromechanical models to estimate biological ankle joint torques in real-time from measured electromyograms (EMGS) and joint angles. We call this 'neuromechanical model-based control' (NMBC). NMBC enabled six individuals to voluntarily control a bilateral ankle exoskeleton across six walking conditions, including all intermediate transitions, i.e., two walking speeds, each performed at three ground elevations. A single subject case-study was carried out on a dexterous locomotion tasks involving moonwalking. NMBC always enabled reducing biological ankle torques, as well as eight ankle muscle EMGs both within (22% torque;12% EMG) and between walking conditions (24% torque; 14% EMG) when compared to non-assisted conditions. Torque and EMG reductions in novel walking conditions indicated that the exoskeleton operated symbiotically, as an exomuscle controlled by the operator.s neuromuscular system. This opens new avenues for the systematic adoption of wearable robots as part of out-of-the-lab medical and occupational settings

Development of Digital Control Systems for Wearable Mechatronic Devices: Applications in Musculoskeletal Rehabilitation of the Upper Limb

The potential for wearable mechatronic systems to assist with musculoskeletal rehabilitation of the upper limb has grown with the technology. One limiting factor to realizing the benefits of these devices as motion therapy tools is within the development of digital control solutions. Despite many device prototypes and research efforts in the surrounding fields, there are a lack of requirements, details, assessments, and comparisons of control system characteristics, components, and architectures in the literature. Pairing this with the complexity of humans, the devices, and their interactions makes it a difficult task for control system developers to determine the best solution for their desired applications. The objective of this thesis is to develop, evaluate, and compare control system solutions that are capable of tracking motion through the control of wearable mechatronic devices. Due to the immaturity of these devices, the design, implementation, and testing processes for the control systems is not well established. In order to improve the efficiency and effectiveness of these processes, control system development and evaluation tools have been proposed. The Wearable Mechatronics-Enabled Control Software framework was developed to enable the implementation and comparison of different control software solutions presented in the literature. This framework reduces the amount of restructuring and modification required to complete these development tasks. An integration testing protocol was developed to isolate different aspects of the control systems during testing. A metric suite is proposed that expands on the existing literature and allows for the measurement of more control characteristics. Together, these tools were used ii ABSTRACT iii to developed, evaluate, and compare control system solutions. Using the developed control systems, a series of experiments were performed that involved tracking elbow motion using wearable mechatronic elbow devices. The accuracy and repeatability of the motion tracking performances, the adaptability of the control models, and the resource utilization of the digital systems were measured during these experiments. Statistical analysis was performed on these metrics to compare between experimental factors. The results of the tracking performances show some of the highest accuracies for elbow motion tracking with these devices. The statistical analysis revealed many factors that significantly impact the tracking performance, such as visual feedback, motion training, constrained motion, motion models, motion inputs, actuation components, and control outputs. Furthermore, the completion of the experiments resulted in three first-time studies, such as the comparison of muscle activation models and the quantification of control system task timing and data storage needs. The successes of these experiments highlight that accurate motion tracking, using biological signals of the user, is possible, but that many more efforts are needed to obtain control solutions that are robust to variations in the motion and characteristics of the user. To guide the future development of these control systems, a national survey was conducted of therapists regarding their patient data collection and analysis methods. From the results of this survey, a series of requirements for software systems, that allow therapists to interact with the control systems of these devices, were collected. Increasing the participation of therapists in the development processes of wearable assistive devices will help to produce better requirements for developers. This will allow the customization of control systems for specific therapies and patient characteristics, which will increase the benefit and adoption rate of these devices within musculoskeletal rehabilitation programs

Neuromechanical and environment aware machine learning tool for human locomotion intent recognition

Current research suggests the emergent need to recognize and predict locomotion modes (LMs) and LM transitions to allow a natural and smooth response of lower limb active assistive devices such as prostheses and orthosis for daily life locomotion assistance. This Master dissertation proposes an automatic and user-independent recognition and prediction tool based on machine learning methods. Further, it seeks to determine the gait measures that yielded the best performance in recognizing and predicting several human daily performed LMs and respective LM transitions. The machine learning framework was established using a Gaussian support vector machine (SVM) and discriminative features estimated from three wearable sensors, namely, inertial, force and laser sensors. In addition, a neuro-biomechanical model was used to compute joint angles and muscle activations that were fused with the sensor-based features. Results showed that combining biomechanical features from the Xsens with environment-aware features from the laser sensor resulted in the best recognition and prediction of LM (MCC = 0.99 and MCC = 0.95) and LM transitions (MCC = 0.96 and MCC = 0.98). Moreover, the predicted LM transitions were determined with high prediction time since their detection happened one or more steps before the LM transition occurrence. The developed framework has potential to improve the assistance delivered by locomotion assistive devices to achieve a more natural and smooth motion assistance.This work has been supported in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant SFRH/BD/108309/2015, and part by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from FCT with the project SmartOs -Controlo Inteligente de um Sistema Ortótico Ativo e Autónomo- under Grant NORTE-01-0145-FEDER-030386, and by the FEDER Funds through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI)—with the Reference Project under Grant POCI-01-0145-FEDER-006941

Human Activity Recognition and Control of Wearable Robots

abstract: Wearable robotics has gained huge popularity in recent years due to its wide applications in rehabilitation, military, and industrial fields. The weakness of the skeletal muscles in the aging population and neurological injuries such as stroke and spinal cord injuries seriously limit the abilities of these individuals to perform daily activities. Therefore, there is an increasing attention in the development of wearable robots to assist the elderly and patients with disabilities for motion assistance and rehabilitation. In military and industrial sectors, wearable robots can increase the productivity of workers and soldiers. It is important for the wearable robots to maintain smooth interaction with the user while evolving in complex environments with minimum effort from the user. Therefore, the recognition of the user's activities such as walking or jogging in real time becomes essential to provide appropriate assistance based on the activity. This dissertation proposes two real-time human activity recognition algorithms intelligent fuzzy inference (IFI) algorithm and Amplitude omega ($A \omega$) algorithm to identify the human activities, i.e., stationary and locomotion activities. The IFI algorithm uses knee angle and ground contact forces (GCFs) measurements from four inertial measurement units (IMUs) and a pair of smart shoes. Whereas, the $A \omega$ algorithm is based on thigh angle measurements from a single IMU. This dissertation also attempts to address the problem of online tuning of virtual impedance for an assistive robot based on real-time gait and activity measurement data to personalize the assistance for different users. An automatic impedance tuning (AIT) approach is presented for a knee assistive device (KAD) in which the IFI algorithm is used for real-time activity measurements. This dissertation also proposes an adaptive oscillator method known as amplitude omega adaptive oscillator ($A\omega AO$) method for HeSA (hip exoskeleton for superior augmentation) to provide bilateral hip assistance during human locomotion activities. The $A \omega$ algorithm is integrated into the adaptive oscillator method to make the approach robust for different locomotion activities. Experiments are performed on healthy subjects to validate the efficacy of the human activities recognition algorithms and control strategies proposed in this dissertation. Both the activity recognition algorithms exhibited higher classification accuracy with less update time. The results of AIT demonstrated that the KAD assistive torque was smoother and EMG signal of Vastus Medialis is reduced, compared to constant impedance and finite state machine approaches. The $A\omega AO$ method showed real-time learning of the locomotion activities signals for three healthy subjects while wearing HeSA. To understand the influence of the assistive devices on the inherent dynamic gait stability of the human, stability analysis is performed. For this, the stability metrics derived from dynamical systems theory are used to evaluate unilateral knee assistance applied to the healthy participants.Dissertation/ThesisDoctoral Dissertation Aerospace Engineering 201