Walk-IT: An Open-Source Modular Low-Cost Smart Rollator.

Rollators are widely used in clinical rehabilitation for gait assessment, but gait analysis usually requires a great deal of expertise and focus from medical staff. Smart rollators can capture gait parameters autonomously while avoiding complex setups. However, commercial smart rollators, as closed systems, can not be modified; plus, they are often expensive and not widely available. This work presents a low cost open-source modular rollator for monitorization of gait parameters and support. The whole system is based on commercial components and its software architecture runs over ROS2 to allow further customization and expansion. This paper describes the overall software and hardware architecture and, as an example of extended capabilities, modules for monitoring dynamic partial weight bearing and for estimation of spatiotemporal gait parameters of clinical interest. All presented tests are coherent from a clinical point of view and consistent with input data.

Proprioceptive Estimation of Forces Using Underactuated Fingers for Robot-Initiated pHRI.

In physical Human-Robot Interaction (pHRI), forces exerted by humans need to be estimated to accommodate robot commands to human constraints, preferences, and needs. This paper presents a method for the estimation of the interaction forces between a human and a robot using a gripper with proprioceptive sensing. Specifically, we measure forces exerted by a human limb grabbed by an underactuated gripper in a frontal plane using only the gripper's own sensors. This is achieved via a regression method, trained with experimental data from the values of the phalanx angles and actuator signals. The proposed method is intended for adaptive shared control in limb manipulation. Although adding force sensors provides better performance, the results obtained are accurate enough for this application. This approach requires no additional hardware: it relies uniquely on the gripper motor feedback-current, position and torque-and joint angles. Also, it is computationally cheap, so processing times are low enough to allow continuous human-adapted pHRI for shared control.

Weight-Bearing Estimation for Cane Users by Using Onboard Sensors.

Mobility is a fundamental requirement for a healthy, active lifestyle. Gait analysis is widely acknowledged as a clinically useful tool for identifying problems with mobility, as identifying abnormalities within the gait profile is essential to correct them via training, drugs, or surgical intervention. However, continuous gait analysis is difficult to achieve due to technical limitations, namely the need for specific hardware and constraints on time and test environment to acquire reliable data. Wearables may provide a solution if users carry them most of the time they are walking. We propose to add sensors to walking canes to assess user's mobility. Canes are frequently used by people who cannot completely support their own weight due to pain or balance issues. Furthermore, in absence of neurological disorders, the load on the cane is correlated with the user condition. Sensorized canes already exist, but often rely on expensive sensors and major device modifications are required. Thus, the number of potential users is severely limited. In this work, we propose an affordable module for load monitoring so that it can be widely used as a screening tool. The main advantages of our module are: (i) it can be deployed in any standard cane with minimal changes that do not affect ergonomics; (ii) it can be used every day, anywhere for long-term monitoring. We have validated our prototype with 10 different elderly volunteers that required a cane to walk, either for balance or partial weight bearing. Volunteers were asked to complete a 10 m test and, then, to move freely for an extra minute. The load peaks on the cane, corresponding to maximum support instants during the gait cycle, were measured while they moved. For validation, we calculated their gait speed using a chronometer during the 10 m test, as it is reportedly related to their condition. The correlation between speed (condition) and load results proves that our module provides meaningful information for screening. In conclusion, our module monitors support in a continuous, unsupervised, nonintrusive way during users' daily routines, plus only mechanical adjustment (cane height) is needed to change from one user to another.

A Social Potential Fields Approach for Self-Deployment and Self-Healing in Hierarchical Mobile Wireless Sensor Networks.

Autonomous mobile nodes in mobile wireless sensor networks (MWSN) allow self-deployment and self-healing. In both cases, the goals are: (i) to achieve adequate coverage; and (ii) to extend network life. In dynamic environments, nodes may use reactive algorithms so that each node locally decides when and where to move. This paper presents a behavior-based deployment and self-healing algorithm based on the social potential fields algorithm. In the proposed algorithm, nodes are attached to low cost robots to autonomously navigate in the coverage area. The proposed algorithm has been tested in environments with and without obstacles. Our study also analyzes the differences between non-hierarchical and hierarchical routing configurations in terms of network life and coverage.

On Gait Analysis Estimation Errors Using Force Sensors on a Smart Rollator.

Gait analysis can provide valuable information on a person's condition and rehabilitation progress. Gait is typically captured using external equipment and/or wearable sensors. These tests are largely constrained to specific controlled environments. In addition, gait analysis often requires experts for calibration, operation and/or to place sensors on volunteers. Alternatively, mobility support devices like rollators can be equipped with onboard sensors to monitor gait parameters, while users perform their Activities of Daily Living. Gait analysis in rollators may use odometry and force sensors in the handlebars. However, force based estimation of gait parameters is less accurate than traditional methods, especially when rollators are not properly used. This paper presents an evaluation of force based gait analysis using a smart rollator on different groups of users to determine when this methodology is applicable. In a second stage, the rollator is used in combination with two lab-based gait analysis systems to assess the rollator estimation error. Our results show that: (i) there is an inverse relation between the variance in the force difference between handlebars and support on the handlebars-related to the user condition-and the estimation error; and (ii) this error is lower than 10% when the variation in the force difference is above 7 N. This lower limit was exceeded by the 95.83% of our challenged volunteers. In conclusion, rollators are useful for gait characterization as long as users really need the device for ambulation.

Rule-Based vs. Behavior-Based Self-Deployment for Mobile Wireless Sensor Networks.

In mobile wireless sensor networks (MWSN), nodes are allowed to move autonomously for deployment. This process is meant: (i) to achieve good coverage; and (ii) to distribute the communication load as homogeneously as possible. Rather than optimizing deployment, reactive algorithms are based on a set of rules or behaviors, so nodes can determine when to move. This paper presents an experimental evaluation of both reactive deployment approaches: rule-based and behavior-based ones. Specifically, we compare a backbone dispersion algorithm with a social potential fields algorithm. Most tests are done under simulation for a large number of nodes in environments with and without obstacles. Results are validated using a small robot network in the real world. Our results show that behavior-based deployment tends to provide better coverage and communication balance, especially for a large number of nodes in areas with obstacles.

The treatment of bacterial biofilms cultivated on knee arthroplasty implants using the bioelectric effect.

Introduction: The formation of bacterial biofilms on knee arthroplasty implants can have catastrophic consequences. The aim of this study was to analyze the effectiveness of the bioelectric effect in the elimination of bacterial biofilms on cultivated knee arthroplasty implants. Methods: A novel device was designed to deliver a bioelectric effect on the surface of knee arthroplasty implants. 4-femoral prosthetic implants were cultivated with a staphylococcus aureus inoculum for 15 days. The components were divided into four different groups: A (not treated), B (normal saline 20-minutes), C (bioelectric effect 10-minutes), D (bioelectric effect 20-minutes). The implants were sonicated, and the detached colonies were quantified as the number of colony-forming unit (CFUs). The implants were sterilised and the process was repeated in a standardized manner four more times, to obtain a total of five samples per group. Results: The number of the CFUs after a 10-minute exposure to the bioelectric effect was of 208.2 ± 240.4, compared with 6,041.6 ± 2010.7 CFUs in group A, representing a decrease of 96.5% ± 4.3 (p = 0.004). And a diminution of 91.8% ± 7.9 compared with 2,051.0 ± 1,364.0 CFUs in group B (p = 0.109). The number of bacterial colonies after a 20-minute exposure to the bioelectric effect was 70 ± 126.7 CFUs, representing a decrease of 98.9% ± 1.9 (p = 0.000) compared with group A. And a decrease of 97.8% ± 3.0 (p = 0.019) compared with group B. Conclusions: The bioelectric effect was effective in the elimination of bacterial biofilm from knee arthroplasty implants. This method could be used in the future as part of conventional surgical procedures.

A Prediction Model for Primary Anterior Cruciate Ligament Injury Using Artificial Intelligence.

BACKGROUND: Supervised machine learning models in artificial intelligence (AI) have been increasingly used to predict different types of events. However, their use in orthopaedic surgery has been limited. HYPOTHESIS: It was hypothesized that supervised learning techniques could be used to build a mathematical model to predict primary anterior cruciate ligament (ACL) injuries using a set of morphological features of the knee. STUDY DESIGN: Cross-sectional study; Level of evidence, 3. METHODS: Included were 50 adults who had undergone primary ACL reconstruction between 2008 and 2015. All patients were between 18 and 40 years of age at the time of surgery. Patients with a previous ACL injury, multiligament knee injury, previous ACL reconstruction, history of ACL revision surgery, complete meniscectomy, infection, missing data, and associated fracture were excluded. We also identified 50 sex-matched controls who had not sustained an ACL injury. For all participants, we used the preoperative magnetic resonance images to measure the anteroposterior lengths of the medial and lateral tibial plateaus as well as the lateral and medial bone slope (LBS and MBS), lateral and medial meniscal height (LMH and MMH), and lateral and medial meniscal slope (LMS and MMS). The AI predictor was created using Matlab R2019b. A Gaussian naïve Bayes model was selected to create the predictor. RESULTS: Patients in the ACL injury group had a significantly increased posterior LBS (7.0° ± 4.7° vs 3.9° ± 5.4°; P = .008) and LMS (-1.7° ± 4.8° vs -4.0° ± 4.2°; P = .002) and a lower MMH (5.5 ± 0.1 vs 6.1 ± 0.1 mm; P = .006) and LMH (6.9 ± 0.1 vs 7.6 ± 0.1 mm; P = .001). The AI model selected LBS and MBS as the best possible predictive combination, achieving 70% validation accuracy and 92% testing accuracy. CONCLUSION: A prediction model for primary ACL injury, created using machine learning techniques, achieved a >90% testing accuracy. Compared with patients who did not sustain an ACL injury, patients with torn ACLs had an increased posterior LBS and LMS and a lower MMH and LMH.

Automatic Assessment of a Rollator-User's Condition During Rehabilitation Using the i-Walker Platform.

Patient condition during rehabilitation has been traditionally assessed using clinical scales. These scales typically require the patient and/or the clinician to rate a number of condition-related items to obtain a final score. This is a time-consuming task, specially if a large number of patients are involved. Furthermore, during rehabilitation, user condition is expected to change steadily in time, so assessment may require to run these scales several times to each user. To save time, much effort has been focused on developing clinical scales that require little time to be completed. This is usually achieved by measuring a reduced set of features, i.e., focusing the scales on specific features of a defined target population (Parkinson's disease, Stroke, and so on). However, these scales still require the therapist's intervention and may be tiresome for patients who have to fill them repeatedly. This paper proposes a novel approach to automatically obtain balance scales from the onboard sensors of a robotic rollator. These sensors are used to extract spatiotemporal gait parameters from patients using the rollator for support. These parameters are derived from the user forces on the rollator handles and its odometry. Resulting parameters are used to predict the Tinetti mobility clinical scale on the fly, without therapist intervention. Our approach has been validated with 19 rollator volunteers with a variety of physical and neurological disabilities at Hospital Civil (Malaga) and Fondazione Santa Lucia (Rome). Clinicians provided traditionally obtained Tinetti scores and the proposed system was used to estimate them on the fly. Results show a small root mean squared prediction error. This method can be used for any rollator user anywhere in everyday walking conditions to obtain the Tinetti scores as often as desired and, hence evaluate their progress.

[Artificial intelligence in medicine: project of a mobile platform in an intelligent environment for the care of disabled and elderly people]. / Intelligenza artificiale in medicina: progetto di una piattaforma mobile inserita in un ambiente intelligente per l'assistenza ai disabili e agli anziani.

A project based on the integration of new technologies and artificial intelligence to develop a device--e-tool--for disabled patients and elderly people is presented. A mobile platform in intelligent environments (skilled-care facilities and home-care), controlled and managed by a multi-level architecture, is proposed to support patients and caregivers to increase self-dependency in activities of daily living.

On the construction of a skill-based wheelchair navigation profile.

Assisted wheelchair navigation is of key importance for persons with severe disabilities. The problem has been solved in different ways, usually based on the shared control paradigm. This paradigm consists of giving the user more or less control on a need basis. Naturally, these approaches require personalization: each wheelchair user has different skills and needs and it is hard to know a priori from diagnosis how much assistance must be provided. Furthermore, since there is no such thing as an average user, sometimes it is difficult to quantify the benefits of these systems. This paper proposes a new method to extract a prototype user profile using real traces based on more than 70 volunteers presenting different physical and cognitive skills. These traces are clustered to determine the average behavior that can be expected from a wheelchair user in order to cope with significant situations. Processed traces provide a prototype user model for comparison purposes, plus a simple method to obtain without supervision a skill-based navigation profile for any user while he/she is driving. This profile is useful for benchmarking but also to determine the situations in which a given user might require more assistance after evaluating how well he/she compares to the benchmark. Profile-based shared control has been successfully tested by 18 volunteers affected by left or right brain stroke at Fondazione Santa Lucia, in Rome, Italy.

Case-based reasoning emulation of persons for wheelchair navigation.

OBJECTIVE: Testing is a key stage in system development, particularly in systems such as a wheelchair, in which the final user is typically a disabled person. These systems have stringent safety requirements, requiring major testing with many different individuals. The best would be to have the wheelchair tested by many different end users, as each disability affects driving skills in a different way. Unfortunately, from a practical point of view it is difficult to engage end users as beta testers. Hence, testing often relies on simulations. Naturally, these simulations need to be as realistic as possible to make the system robust and safe before real tests can be accomplished. This work presents a tool to automatically test wheelchairs through realistic emulation of different wheelchair users. METHODS AND MATERIALS: Our approach is based on extracting meaningful data from real users driving a power wheelchair autonomously. This data is then used to train a case-based reasoning (CBR) system that captures the specifics of the driver via learning. The resulting case-base is then used to emulate the driving behavior of that specific person in more complex situations or when a new assistive algorithm needs to be tested. CBR returns user's motion commands appropriate for each specific situation to add the human component to shared control systems. RESULTS: The proposed system has been used to emulate several power wheelchair users presenting different disabilities. Data to create this emulation was obtained from previous wheelchair navigation experiments with 35 volunteer in-patients presenting different degrees of disability. CBR was trained with a limited number of scenarios for each volunteer. Results proved that: (i) emulated and real users returned similar paths in the same scenario (maximum and mean path deviations are equal to 23 and 10cm, respectively) and similar efficiency; (ii) we established the generality of our approach taking a new path not present in the training traces; (iii) the emulated user is more realistic - path and efficiency are less homogeneous and smooth - than potential field approaches; and (iv) the system adequately emulates in-patients - maximum and mean path deviations are equal to 19 and 8.3cm approximately and efficiencies are similar - with specific disabilities (apraxia and dementia) obtaining different behaviors during emulation for each of the in-patients, as expected. CONCLUSIONS: The proposed system adequately emulates the driving behavior of people with different disabilities in indoor scenarios. This approach is suitable to emulate real users' driving behaviors for early testing stages of assistive navigation systems.

Wheelchair collaborative control for disabled users navigating indoors.

OBJECTIVE: Mobility is of key importance for autonomous living. Persons with severe disabilities may be assisted by robotic wheelchairs when manual control is not possible. However, these persons should contribute to control as much as they can to avoid loss of residual skills and frustration. Traditionally, wheelchair shared control approaches either give control to person or robot depending on the situation. METHODS AND MATERIALS: We propose a new shared control technique where robot and person contribute simultaneously to control. Their commands are weighted according to their respective local efficiencies and then combined via a reactive navigation strategy. Thus, assistance adapts to the user's needs. We refer to this approach as collaborative control. RESULTS: Collaborative control was tested in a home environment in Fondazione Santa Lucia (Rome) by 18 volunteers presenting different degrees of physical and cognitive disability. All of them successfully finished a complex test path with assistance. Both users and caregivers' opinion on the system was very positive. Acceptance was very good according to the psychosocial impact of assistive devices scale. CONCLUSIONS: Collaborative control adapts to the person's needs and assists him/her when necessary, locally compensating any problem related to specific disabilities. An ANOVA returned a p-value of 0.016, meaning that there is significant improvement in task performance when collaborative control is used.

Biometrically modulated collaborative control for an assistive wheelchair.

To operate a wheelchair, people with severe physical disabilities may require assistance, which can be provided by robotization. However, medical experts report that an excess of assistance may lead to loss of residual skills, so that it is important to provide just the right amount of assistance. This work proposes a collaborative control system based on weighting the robot's and the user's commands by their respective efficiency to reactively obtain an emergent controller. Thus, the better the person operates, the more control he/she gains. Tests with volunteers have proven, though, that some users may require extra assistance when they become stressed. Hence, we propose a controller that can change the amount of support taking into account supplementary biometric data. In this work, we use an off-the-shelf wearable pulse oximeter. Experiments have demonstrated that volunteers could use our wheelchair in a more efficient way due to the proposed biometric modulated collaborative control.

Situated robotics: from learning to teaching by imitation.

This paper presents an approach to imitation learning in robotics focusing on low level behaviours, so that they do not need to be encoded into sets and rules, but learnt in an intuitive way. Its main novelty is that, rather than trying to analyse natural human actions and adapting them to robot kinematics, humans adapt themselves to the robot via a proper interface to make it perform the desired action. As an example, we present a successful experiment to learn a purely reactive navigation behaviour using robotic platforms. Using Case Based Reasoning, the platform learns from a human driver how to behave in the presence of obstacles, so that no kinematics studies or explicit rules are required.