Real-time Foot Research Articles

Wearable Foot Pressure Sensing System for Fallen Arch Analysis

The main purpose of this research is to distinguish the extent of fallen arch by analyzing the center of foot pressure when people are walking. Fallen arch is often called the flatfoot. There are two types of flatfoot, the rigid flatfoot and the acquired flatfoot. The rigid flatfoot is because of bones formation, and people often get rigid flatfoot when they are young. The acquired flatfoot occurs usually on adults because of the walking postures, shoe types, weight and so on. Since these reasons, it is possible that we can improve or prevent the acquired flatfoot through wearing orthotic insoles or changing the walking posture. That’s the reason why our research is focusing on the acquired flatfoot. In this research, we develop a wearable foot pressure sensing system, consist of wireless Bluetooth transmit module and sensing insoles. The wireless Bluetooth transmit module helps to accomplish our measuring system as a wearable device. The sampling rate of wireless Bluetooth transmit module is one thousand samples per second. As a result, we can get a very detailed track on the walking motions. The sensing insoles are made of triboelectric nanogenerator (TENG) technology. The working principle of TENG is based on the coupling of triboelectric effect and electrostatic induction. Depending on the property of triboelectric generation of different materials and through continuous contact and separation, there is an electrostatic induction and current flow between the triboelectric layers and the electrodes, thus causing the potential difference. By detecting the potential differences, we can get the signals of pressure, so we can calculate the center of foot pressure and compare with the standard to check which walking posture we can apply to improve the fallen arch and find out where the center of foot pressure should be placed. Using this system, we can get the real-time foot pressure information and collect the information anytime and anywhere, accomplishing the smart medical.

Real-Time Foot Tracking and Gait Evaluation with Geometric Modeling.

Gait evaluation is important in gait rehabilitation and assistance to monitor patient’s balance status and assess recovery performance. Recent technologies leverage on vision-based systems with high portability and low operational complexity. In this paper, we propose a new vision-based foot tracking algorithm specially catering to overground gait assistive devices, which often have limited view of the users. The algorithm models the foot and the shank of the user using simple geometry. Through cost optimization, it then aligns the models to the point cloud, showing the back view of the user’s lower limbs. The system outputs the poses of the feet, which are used to compute the spatial-temporal gait parameters. Seven healthy young subjects are recruited to perform overground and treadmill walking trials. The results of the algorithm are compared with the motion capture system and a third-party gait analysis software. The algorithm has a fitting rotational and translational errors of less than 20 degrees and 33 mm, respectively, for 0.4 m/s walking speed. The gait detection F1 score achieves more than 96.8%. The step length and step width errors are around 35 mm, while the cycle time error is less than 38 ms. The proposed algorithm provides a fast, contactless, portable, and cost-effective gait evaluation method without requiring the user to wear any customized footwear.

Adaptive Feet for Quadrupedal Walkers

The vast majority of state-of-the-art walking robots employ flat or ball feet for locomotion, presenting limitations while stepping on obstacles, slopes, or unstructured terrain. Moreover, traditional feet for quadrupeds lack sensing systems that are able to provide information about the environment and about the foot interaction with the surroundings. This further diminishes their value. Inspired by our previous work on soft feet for bipedal robots, we present the SoftFoot-Q, an articulated adaptive foot for quadrupeds. This device is conceived to be robust and able to overcome the limitations of currently employed feet. The core idea behind our adaptive foot design is first introduced and validated through a simplified mathematical formulation of the problem. Subsequently, we present the chosen mechanical implementation to attempt overcoming current limitations. The realized prototype of adaptive foot is integrated and tested on the compliantly actuated quadrupedal robot ANYmal together with an ROS-based real-time foot pose reconstruction software. Both extensive field tests and indoor experiments show noticeable performance improvements, in terms of reduced slippage of the robot, with respect to both flat and ball feet.

A Novel Instrumented Outsole for Real-Time Foot Kinematic Measurements: Validation Across Different Speeds and Simulated Foot Landing

Neuromuscular disorders, especially in Cerebral Palsy patients, lead to foot deformities and affect foot biomechanics leading to compromised gait. Thus, the foot kinematic measurement is of particular interest to understand and characterize the walking pattern. The present work aims to develop a wearable instrument to measure foot kinematics such as foot-to-ground angle in three-dimensional planes and to measure the foot clearance, i.e., toe and heel clearances. A template-based outsole was developed that incorporated optical distance sensors located anatomically on the outsole and a magnetometer to measure the foot kinematics. The developed system was validated against the reference marker-based motion capture system from Noraxon, USA. The data from eight able-bodied participants were acquired simultaneously from both the systems (developed and the reference system) at three different walking speeds. A center of pressure (CoP) based feedback was presented to the participants to shift the sagittal CoP anteriorly, posteriorly and medially to simulate the walking pattern of patients with the neuromuscular disorder at three different foot landing strategies. It was found that more than or equal to 96.48% values of the differences of both the systems is between the limit of agreement, values of Pearson’s correlation coefficient were more than or equal to 0.76 and values of root mean square error were less than or equal to 3.45 degrees. Furthermore, a wireless wristband was developed and validated for real-time vibrotactile feedback. Since instrumented outsole is wearable, low-cost, easy to use and implement, it has a great potential in real-time foot kinematic measurements.

Wearable Technology May Assist in Retraining Foot Strike Patterns in Previously Injured Military Service Members: A Prospective Case Series

A rearfoot strike (RFS) pattern with increased average vertical loading rates (AVLR) while running has been associated with injury. This study evaluated the ability of an instrumented sock, which provides real-time foot strike and cadence audio biofeedback, to transition previously injured military service members from a RFS to a non-rearfoot strike (NRFS) running pattern. Nineteen RFS runners (10 males, 9 females) were instructed to wear the instrumented socks to facilitate a change in foot strike while completing an independent walk-to-run progression and lower extremity exercise program. Kinetic data were collected during treadmill running while foot strike was determined using video analysis at initial (T1), post-intervention (T2), and follow-up (T3) data collections. Nearly all runners (18/19) transitioned to a NRFS pattern following intervention (8 ± 2.4 weeks after the initial visit). Most participants (16/18) maintained the transition at follow-up (5 ± 0.8 weeks after the post-intervention visit). AVLR of the involved and uninvolved limb decreased 29% from initial [54.7 ± 13.2 bodyweights per sec (BW/s) and 55.1 ± 12.7 BW/s] to post-intervention (38.7 ± 10.1 BW/s and 38.9 ± 10.0 BW/s), respectively. This effect persisted 5-weeks later at follow-up, representing an overall 30% reduction on the involved limb and 24% reduction on the uninvolved limb. Cadence increased from the initial to the post-intervention time-point (p = 0.045); however, this effect did not persist at follow-up (p = 0.08). With technology provided feedback from instrumented socks, approximately 90% of participants transitioned to a NRFS pattern, decreased AVLR, reduced stance time and maintained these running adaptations 5-weeks later.

Smart Insole-Based Indoor Localization System for Internet of Things Applications

With the development of Internet of Things (IoT), indoor localization has been a research focus in recent years. For inertial measurement unit (IMU)-based indoor localization method, zero velocity update (ZUPT) uses the known velocity at stationary epoch as a benchmark to calibrate the velocity drift. However, stationary epoch only takes up 24% of a whole gait cycle time, and the velocity drift at the remaining 76% time is usually estimated according to an assumption that velocity has a linear drift over time, which would introduce errors. In this paper, a two-step velocity calibration method was proposed based on human gait characteristics with Smart Insole: known velocity update (KUPT) and double-foot position calibration (DFPC). KUPT could measure the velocity from heel-strike to toe-off based on the recorded real-time foot angle and the shoe dimensions, which increases the time period when the velocity could be measured from 24% to 62% of a whole gait cycle time. DFPC method could fuse the position information of both feet based on the symmetrical characteristic of human gait to further increase the reliability of the localization results. The statistical result of a 20 times 20-m walking experiment showed that KUPT method was more accurate and reliable than ZUPT method for both feet, and DFPC method could further improve the result of KUPT method. Another experiment about walking in an indoor environment for 91 m showed that the proposed KUPT+DFPC method had an error of about 0.78 m which is acceptable for most IoT applications.

Application of Signed Distance Function Neural Network in Real-Time Feet Tracking.

Loss of locomotion is one major problem faced by the elderly currently. Various rehabilitation technologies have been developed to assist them in recovering walking capability. Gait monitoring is an important aspect of lower-limb function rehabilitation. By observing the walking behaviour of a patient, the stability and recovery progress can be evaluated. Despite gait is often measured by motion capture and force-based measurement, these methods are costly and non-portable, whereas inertial measurement units (IMUs) require the attachment of sensors onto the subject's body. A few contactless measurements have been proposed, however, none of them views the feet from the back, making it non-trivial to transfer the method to over ground rehabilitation robots. This paper proposes a method to track the poses of the feet in real time using a novel deep neural network, termed SDF-Net, that models the signed distance function (SDF) of an object. Independent of the viewing angle, the algorithm receives the colour and depth images of the feet as input and computes the pose of the feet. The tracking accuracy is evaluated by having a subject to perform various actions with the feet; the dynamic errors are found to be less than 9 mm and 8 degrees for position and orientation errors respectively, which are better than the state-of-arts reviewed.

Real-time foot clearance biofeedback to assist gait rehabilitation following stroke: a randomized controlled trial protocol

BackgroundThe risk of falling is significantly higher in people with chronic stroke and it is, therefore, important to design interventions to improve mobility and decrease falls risk. Minimum toe clearance (MTC) is the key gait cycle event for predicting tripping-falls because it occurs mid-swing during the walking cycle where forward velocity of the foot is maximum.High forward velocity coupled with low MTC increases the probability of unanticipated foot-ground contacts. Training procedures to increase toe-ground clearance (MTC) have potential, therefore, as a falls-prevention intervention. The aim of this project is to determine whether augmented sensory information via real-time visual biofeedback during gait training can increase MTC.MethodsParticipants will be aged > 18 years, have sustained a single stroke (ischemic or hemorrhagic) at least six months previously, able to walk 50 m independently, and capable of informed consent. Using a secure web-based application (REDCap), 150 participants will be randomly assigned to either no-feedback (Control) or feedback (Experimental) groups; all will receive 10 sessions of treadmill training for up to 10 min at a self-selected speed over 5–6 weeks. The intervention group will receive real-time, visual biofeedback of MTC during training and will be asked to modify their gait pattern to match a required “target” criterion. Biofeedback is continuous for the first six sessions then progressively reduced (faded) across the remaining four sessions. Control participants will walk on the treadmill without biofeedback. Gait assessments are conducted at baseline, immediately following the final training session and then during follow-up, at one, three, and six months. The primary outcome measure is MTC. Monthly falls calendars will also be collected for 12 months from enrolment.DiscussionThe project will contribute to understanding how stroke-related changes to sensory and motor processes influence gait biomechanics and associated tripping risk. The research findings will guide our work in gait rehabilitation following stroke and may reduce falls rates. Treadmill training procedures incorporating continuous real-time feedback may need to be modified to accommodate stroke patients who have greater difficulties with treadmill walking.Trial registrationAustralia New Zealand Clinical Trials Registry, ACTRN12617000250336. Registered on 17 February 2017.

Using conversation analysis to inform role play and simulated interaction in communications skills training for healthcare professionals: identifying avenues for further development through a scoping review

BackgroundThis paper responds to previously published debate in this journal around the use of sociolinguistic methods in communication skills training (CST), which has raised the significant question of how far consultations with simulated patients reflect real clinical encounters. This debate concluded with a suggestion that sociolinguistic methods offer an alternative analytic lens for evaluating CST. We demonstrate here that the utility of sociolinguistic methods in CST is not limited to critique, but also presents an important tool for development and delivery.MethodsFollowing a scoping review of the use of role play and simulated interaction in CST for healthcare professionals, we consider the use of the specific sociolinguistic approach of conversation analysis (CA), which has been applied to the study of health communication in a wide range of settings, as well as to the development of training.DiscussionRole play and simulated interaction have been criticised by both clinicians and sociolinguists for a lack of authenticity as compared to real life interactions. However they contain a number of aspects which healthcare professionals report finding particularly useful: the need to think on one’s feet in real time, as in actual interaction with patients; the ability to receive feedback on the simulation; and the ability to watch and reflect on how others approach the same simulation task in real time. Since sociolinguistic approaches can help to identify inauthenticity in role play and simulation, they can also be used to improve authenticity. Analysis of real-life interactions using sociolinguistic methods, and CA in particular, can identify actual interactional practices that are used by particular patient groups. These practices can then be used to inform the training of actors simulating patients. In addition, the emphasis of CA on talk as joint activity means that proper account can be taken of the way in which simulated interaction is co-constructed between simulator and trainee.SummaryWe suggest that as well as identifying potential weaknesses in current role play and simulation practice, conversation analysis offers the potential to enhance and develop the authenticity of these training methods.

Quantitative Measurements of Real-Time Foot Perfusion in Patients with Critical Limb Ischemia

Quantitative Measurements of Real-Time Foot Perfusion in Patients with Critical Limb Ischemia

Design of Low Cost Smart Insole for Real Time Measurement of Plantar Pressure

Abstract Real time plantar pressure provides information critical to the understanding of gait mechanics and has a wide range of applications. In this study, smart insoles were designed and developed to measure real time foot plantar pressure. Key features of the insoles included cost-effectiveness, good working pressure detection range, wireless data transfer and real-time data analysis. Calibration of the sensing material was done and the resulting accuracy of the insoles was compared to that of a Kistler force plate, achieving an r2 value of 0.981. Real-time visualization of pressure mapping was incorporated to enable intuitive understanding of relative plantar pressure distribution.

Validation of a Method for Real Time Foot Position and Orientation Tracking With Microsoft Kinect Technology for Use in Virtual Reality and Treadmill Based Gait Training Programs

The use of virtual reality for the provision of motor-cognitive gait training has been shown to be effective for a variety of patient populations. The interaction between the user and the virtual environment is achieved by tracking the motion of the body parts and replicating it in the virtual environment in real time. In this paper, we present the validation of a novel method for tracking foot position and orientation in real time, based on the Microsoft Kinect technology, to be used for gait training combined with virtual reality. The validation of the motion tracking method was performed by comparing the tracking performance of the new system against a stereo-photogrammetric system used as gold standard. Foot position errors were in the order of a few millimeters (average RMSD from 4.9 to 12.1 mm in the medio-lateral and vertical directions, from 19.4 to 26.5 mm in the anterior-posterior direction); the foot orientation errors were also small (average %RMSD from 5.6% to 8.8% in the medio-lateral and vertical directions, from 15.5% to 18.6% in the anterior-posterior direction). The results suggest that the proposed method can be effectively used to track feet motion in virtual reality and treadmill-based gait training programs.

A Real Time Foot Pressure Measurement for Early Detection of Ulcer Formation in Diabetics Patients Using Labview

Diabetic foot ulcer is one of the major complications caused due to Diabetes Mellitus. Elevation of blood sugar level above normal conditions causes several vascular problems (constriction of blood vessels) which lead to improper circulation of blood to the feet. This leads to gangrene formation and loss of sensation in the patient's foot. Majority of the diabetics ignore this problem, which ultimately leads to lower leg amputation. Our project aims to identify the patients who are likely to develop diabetic foot ulcers at an early stage. We accomplish this by fixing pressure sensors in six pressure points of the foot. The foot pressure readings are converted into corresponding voltage output by the sensor. These voltage readings are amplified using an amplification unit. The voltage data are read using a data acquisition device which is connected to a personal computer. We use LabVIEW for analyzing the voltage readings in a graphical format. The peak voltage reading indicates that the patient might be prone to develop a foot ulcer in that particular area of the foot.