Spatio-temporal gait analysis based on human-smart rollator interaction

The ability to walk is typically related to several biomechanical components that are involved in the gait cycle (or stride), including free mobility of joints, particularly in the legs; coordination of muscle activity in terms of timing and intensity; and normal sensory input, such as vision and vestibular system. As people age, they tend to slow their gait speed, and their balance is also affected. Also, the retirement from the working life and the consequent reduction of physical and social activity contribute to the increased incidence of falls in older adults. Moreover, older adults suffer different kinds of cognitive decline, such as dementia or attention problems, which also accentuate gait disorders and its consequences. In this paper we present a methodology for gait identification using the on-board sensors of a smart rollator: the i-Walker. This technique provides the number of steps performed in walking exercises, as well as the time and distance travelled for each stride. It also allows to extract spatio-temporal metrics used in medical gait analysis from the interpretation of the interaction between the individual and the i-Walker. In addition, two metrics to assess users’ driving skills, laterality and directivity, are proposed.Peer ReviewedPostprint (author's final draft

Evaluation studies of robotic rollators by the user perspective: A systematic review

Background: Robotic rollators enhance the basic functions of established devices by technically advanced physical, cognitive, or sensory support to increase autonomy in persons with severe impairment. In the evaluation of such Ambient Assisted Living solutions, both the technical and user perspectives are important to prove usability, effectiveness, and safety, and to ensure adequate device application.Objective: The aim of this systematic review is to summarize the methodology of studies evaluating robotic rollators with focus on the user perspective and to give recommendations for future evaluation studies.Methods: A systematic literature search up to December 31, 2014 was conducted based on the Cochrane Review methodology using the electronic databases PubMed and IEEE Xplore. Articles were selected according to the following inclusion criteria: Evaluation studies of robotic rollators documenting human-robot interaction, no case reports, published in English language.Results: Twenty-eight studies were identified that met the predefined inclusion criteria. Large heterogeneity in the definitions of the target user group, study populations, study designs, and assessment methods was found across the included studies. No generic methodology to evaluate robotic rollators could be identified. We found major methodological shortcomings related to insufficient sample descriptions and sample sizes, and lack of appropriate, standardized and validated assessment methods. Long-term use in habitual environment was also not evaluated.Conclusions: Apart from the heterogeneity, methodological deficits in most of the identified studies became apparent. Recommendations for future evaluation studies include: clear definition of target user group, adequate selection of subjects, inclusion of other assistive mobility devices for comparison, evaluation of the habitual use of advanced prototypes, adequate assessment strategy with established, standardized and validated methods, and statistical analysis of study results. Assessment strategies may additionally focus on specific functionalities of the robotic rollators allowing an individually tailored assessment of innovative features to document their added value

Navigation system using passive collaborative control adapted to user profile for a rollator device

In order to achieve this goal, research in different areas has been necessary. First, a methodology to provide human-like platform motion in reactive navigation algorithms has been proposed to improve user acceptance of help. Then, work has focused on gait analysis and user's condition estimation using only onboard sensors. In addition, a new methodology to evaluate fall risk using only onboard sensors while users walk has been proposed to balance the contribution of user and robot to control. All proposed subsystems have been validated with a set of volunteers at two rehabilitation hospitals: Fondazione Santa Lucia (Rome) and Hospital Regional Universitario (Malaga). Volunteers presented a wide variety of physical and cognitive disabilities. Tests with healthy volunteers have been discarded from the beginning to avoid a sampling bias error. Obtained results have shown that the proposed system can be used for: i) reactively generating human-like trajectories that outperforms all other tested algorithms in terms of likeness to human paths and success rate; ii) monitoring gait and user's condition while users walk using only on-board sensors; and iii) evaluating fall risk without wearable sensors nor ambient sensors. This thesis open a number of open research lines: i) user condition estimation can be extended to another medical scales; ii) the method to reactively generate human-like-trajectories can be extended to add deliberative human-adapted-path-planning; and iii) the fall risk estimator can be extended to a fall risk predictor.Rollators provide autonomy to persons with mobility impairments. These platforms can be used while people perform their Activities of Daily Living in order to provide support and/or balance. Also, they can be used during the rehabilitation process to strengthen the lower limbs or to provide balance before users can progress to canes or crutches. Rollators have a limited set of personalization options, but they are usually related to the users' body size. Hence, people who need extra typically have to choose a wheelchair instead. This transition to a wheelchair limits users' movements and it increases their disuse syndrome because they do not exercise their lower limbs. Hence, it is a priority to extent the use of rollator platforms as much as possible by adapting help to people who can not use a conventional rollator on their own. Technological enhancements can be added to rollator to expand their use to a larger population. For example, force sensors on handlebars provide information about users' weight bearing. This information can be used during rehabilitation to control their partial weight-bearing. Encoders on wheels may also provide useful information about the walking speed, which is a well know estimator of fall risk. In addition to monitorization, motors can be attached to the wheels for assistance, e.g. to reduce effort while ascending slopes. This thesis focuses on creating a navigation system for a robotized rollator, which includes weight bearing sensors, encoders and wheel motors. The navigation system relies on passive collaborative control to continuously combine user and system commands in a seamless way. The main contribution of this work is adaptation to the user's needs through continuous, transparent monitorization and profile estimation

Extraction of user's navigation commands from upper body force interaction in walker assisted gait

<p>Abstract</p> <p>Background</p> <p>The advances in technology make possible the incorporation of sensors and actuators in rollators, building safer robots and extending the use of walkers to a more diverse population. This paper presents a new method for the extraction of navigation related components from upper-body force interaction data in walker assisted gait. A filtering architecture is designed to cancel: (i) the high-frequency noise caused by vibrations on the walker's structure due to irregularities on the terrain or walker's wheels and (ii) the cadence related force components caused by user's trunk oscillations during gait. As a result, a third component related to user's navigation commands is distinguished.</p> <p>Results</p> <p>For the cancelation of high-frequency noise, a Benedict-Bordner g-h filter was designed presenting very low values for Kinematic Tracking Error ((2.035 ± 0.358)·10^-2<it>kgf</it>) and delay ((1.897 ± 0.3697)·10¹<it>ms</it>). A <it>Fourier Linear Combiner </it>filtering architecture was implemented for the adaptive attenuation of about 80% of the cadence related components' energy from force data. This was done without compromising the information contained in the frequencies close to such notch filters.</p> <p>Conclusions</p> <p>The presented methodology offers an effective cancelation of the undesired components from force data, allowing the system to extract in real-time voluntary user's navigation commands. Based on this real-time identification of voluntary user's commands, a classical approach to the control architecture of the robotic walker is being developed, in order to obtain stable and safe user assisted locomotion.</p

Extraction of user's navigation commands from upper body force interaction in walker assisted gait

<p>Abstract</p> <p>Background</p> <p>The advances in technology make possible the incorporation of sensors and actuators in rollators, building safer robots and extending the use of walkers to a more diverse population. This paper presents a new method for the extraction of navigation related components from upper-body force interaction data in walker assisted gait. A filtering architecture is designed to cancel: (i) the high-frequency noise caused by vibrations on the walker's structure due to irregularities on the terrain or walker's wheels and (ii) the cadence related force components caused by user's trunk oscillations during gait. As a result, a third component related to user's navigation commands is distinguished.</p> <p>Results</p> <p>For the cancelation of high-frequency noise, a Benedict-Bordner g-h filter was designed presenting very low values for Kinematic Tracking Error ((2.035 ± 0.358)·10^-2<it>kgf</it>) and delay ((1.897 ± 0.3697)·10¹<it>ms</it>). A <it>Fourier Linear Combiner </it>filtering architecture was implemented for the adaptive attenuation of about 80% of the cadence related components' energy from force data. This was done without compromising the information contained in the frequencies close to such notch filters.</p> <p>Conclusions</p> <p>The presented methodology offers an effective cancelation of the undesired components from force data, allowing the system to extract in real-time voluntary user's navigation commands. Based on this real-time identification of voluntary user's commands, a classical approach to the control architecture of the robotic walker is being developed, in order to obtain stable and safe user assisted locomotion.</p

Human-Robot Interaction Strategies for Walker-Assisted Locomotion

Neurological and age-related diseases affect human mobility at different levels causing partial or total loss of such faculty. There is a significant need to improve safe and efficient ambulation of patients with gait impairments. In this context, walkers present important benefits for human mobility, improving balance and reducing the load on their lower limbs. Most importantly, walkers induce the use of patients residual mobility capacities in different environments. In the field of robotic technologies for gait assistance, a new category of walkers has emerged, integrating robotic technology, electronics and mechanics. Such devices are known as robotic walkers, intelligent walkers or smart walkers One of the specific and important common aspects to the field of assistive technologies and rehabilitation robotics is the intrinsic interaction between the human and the robot. In this thesis, the concept of Human-Robot Interaction (HRI) for human locomotion assistance is explored. This interaction is composed of two interdependent components. On the one hand, the key role of a robot in a Physical HRI (pHRI) is the generation of supplementary forces to empower the human locomotion. This involves a net flux of power between both actors. On the other hand, one of the crucial roles of a Cognitive HRI (cHRI) is to make the human aware of the possibilities of the robot while allowing him to maintain control of the robot at all times. This doctoral thesis presents a new multimodal human-robot interface for testing and validating control strategies applied to a robotic walkers for assisting human mobility and gait rehabilitation. This interface extracts navigation intentions from a novel sensor fusion method that combines: (i) a Laser Range Finder (LRF) sensor to estimate the users legs kinematics, (ii) wearable Inertial Measurement Unit (IMU) sensors to capture the human and robot orientations and (iii) force sensors measure the physical interaction between the humans upper limbs and the robotic walker. Two close control loops were developed to naturally adapt the walker position and to perform body weight support strategies. First, a force interaction controller generates velocity outputs to the walker based on the upper-limbs physical interaction. Second, a inverse kinematic controller keeps the walker within a desired position to the human improving such interaction. The proposed control strategies are suitable for natural human-robot interaction as shown during the experimental validation. Moreover, methods for sensor fusion to estimate the control inputs were presented and validated. In the experimental studies, the parameters estimation was precise and unbiased. It also showed repeatability when speed changes and continuous turns were performed

Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures

The demographic shift of the population towards an increase in the number of elderly citizens, together with the sedentary lifestyle we are adopting, is reflected in the increasingly debilitated physical health of the population. The resulting physical impairments require rehabilitation therapies which may be assisted by the use of wearable sensors or body area network sensors (BANs). The use of novel technology for medical therapies can also contribute to reducing the costs in healthcare systems and decrease patient overflow in medical centers. Sensors are the primary enablers of any wearable medical device, with a central role in eHealth architectures. The accuracy of the acquired data depends on the sensors; hence, when considering wearable and BAN sensing integration, they must be proven to be accurate and reliable solutions. This book is a collection of works focusing on the current state-of-the-art of BANs and wearable sensing devices for physical rehabilitation of impaired or debilitated citizens. The manuscripts that compose this book report on the advances in the research related to different sensing technologies (optical or electronic) and body area network sensors (BANs), their design and implementation, advanced signal processing techniques, and the application of these technologies in areas such as physical rehabilitation, robotics, medical diagnostics, and therapy

Biomechanics beyond the lab: Remote technology for osteoarthritis patient data—A scoping review

The objective of this project is to produce a review of available and validated technologies suitable for gathering biomechanical and functional research data in patients with osteoarthritis (OA), outside of a traditionally fixed laboratory setting. A scoping review was conducted using defined search terms across three databases (Scopus, Ovid MEDLINE, and PEDro), and additional sources of information from grey literature were added. One author carried out an initial title and abstract review, and two authors independently completed full-text screenings. Out of the total 5,164 articles screened, 75 were included based on inclusion criteria covering a range of technologies in articles published from 2015. These were subsequently categorised by technology type, parameters measured, level of remoteness, and a separate table of commercially available systems. The results concluded that from the growing number of available and emerging technologies, there is a well-established range in use and further in development. Of particular note are the wide-ranging available inertial measurement unit systems and the breadth of technology available to record basic gait spatiotemporal measures with highly beneficial and informative functional outputs. With the majority of technologies categorised as suitable for part-remote use, the number of technologies that are usable and fully remote is rare and they usually employ smartphone software to enable this. With many systems being developed for camera-based technology, such technology is likely to increase in usability and availability as computational models are being developed with increased sensitivities to recognise patterns of movement, enabling data collection in the wider environment and reducing costs and creating a better understanding of OA patient biomechanical and functional movement data