Assessing Walking Strategies Using Insole Pressure Sensors for Stroke Survivors

Insole pressure sensors capture the different forces exercised over the different parts of the sole when performing tasks standing up such as walking. Using data analysis and machine learning techniques, common patterns and strategies from different users to achieve different tasks can be automatically extracted. In this paper, we present the results obtained for the automatic detection of different strategies used by stroke survivors when walking as integrated into an Information Communication Technology (ICT) enhanced Personalised Self-Management Rehabilitation System (PSMrS) for stroke rehabilitation. Fourteen stroke survivors and 10 healthy controls have participated in the experiment by walking six times a distance from chair to chair of approximately 10 m long. The Rivermead Mobility Index was used to assess the functional ability of each individual in the stroke survivor group. Several walking strategies are studied based on data gathered from insole pressure sensors and patterns found in stroke survivor patients are compared with average patterns found in healthy control users. A mechanism to automatically estimate a mobility index based on the similarity of the pressure patterns to a stereotyped stride is also used. Both data gathered from stroke survivors and healthy controls are used to evaluate the proposed mechanisms. The output of trained algorithms is applied to the PSMrS system to provide feedback on gait quality enabling stroke survivors to self-manage their rehabilitation

Sensor optimization in smart insoles for post-stroke gait asymmetries using total variation and L1 distances

By deploying pressure sensors on insoles, the forces exerted by the different parts of the foot when performing tasks standing up can be captured. The number and location of sensors to use are important factors in order to enhance the accuracy of parameters used in assessment while minimizing the cost of the device by reducing the number of deployed sensors. Selecting the best locations and the required number of sensors depends on the application and the features that we want to assess. In this paper, we present a computational process to select the optimal set of sensors to characterize gait asymmetries and plantar pressure patterns for stroke survivors based upon the total variation and L1 distances. The proposed mechanism is ecologically validated in a real environment with 14 stroke survivors and 14 control users. The number of sensors is reduced to 4, minimizing the cost of the device both for commercial users and companies and enhancing the cost to benefit ratio for its uptake from a national healthcare system. The results show that the sensors that better represent the gait asymmetries for healthy controls are the sensors under the big toe and midfoot and the sensors in the forefoot and midfoot for stroke survivors. The results also show that all four regions of the foot (toes, forefoot, midfoot, and heel) play an important role for plantar pressure pattern reconstruction for stroke survivors, while the heel and forefoot region are more prominent for healthy controls

Assessing gait impairments based on auto-encoded patterns of mahalanobis distances from consecutive steps

Proceedings of: 14th conference of the Association for the Advancement of Assistive Technology in Europe (AAATE 2017), Sheffield (UK), 12-15th September 2017.Insole pressure sensors capture the force distribution patterns during the stance phase while walking. By comparing patterns obtained from healthy individuals to patients suffering different medical conditions based on a given similarity measure, automatic impairment indexes can be computed in order to help in applications such as rehabilitation. This paper uses the data sensed from insole pressure sensors for a group of healthy controls to train an auto-encoder using patterns of stochastic distances in series of consecutive steps while walking at normal speeds. Two experiment groups are compared to the healthy control group: a group of patients suffering knee pain and a group of post-stroke survivors. The Mahalanobis distance is computed for every single step by each participant compared to the entire dataset sensed from healthy controls. The computed distances for consecutive steps are fed into the previously trained autoencoder and the average error is used to assess how close the walking segment is to the autogenerated model from healthy controls. The results show that automatic distortion indexes can be used to assess each participant as compared to normal patterns computed from healthy controls. The stochastic distances observed for the group of stroke survivors are bigger than those for the people with knee pain.The research leading to these results has received funding from the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R (MINECO), funded by the Spanish Agencia Estatal de Investigación (AEI), and the “ANALYTICS USING SENSOR DATA FOR FLATCITY” project TIN2016-77158-C4-1-R (MINECO/ ERDF, EU) funded by the Spanish Agencia Estatal de Investigación (AEI) and the European Regional Development Fund (ERDF)

A Personalized Self-Management Rehabilitation System for Stroke Survivors: A Quantitative Gait Analysis Using a Smart Insole.

Background: In the United Kingdom, stroke is the single largest cause of adult disability and results in a cost to the economy of £8.9 billion per annum. Service needs are currently not being met; therefore, initiatives that focus on patient-centered care that promote long-term self-management for chronic conditions should be at the forefront of service redesign. The use of innovative technologies and the ability to apply these effectively to promote behavior change are paramount in meeting the current challenges. Objective: Our objective was to gain a deeper insight into the impact of innovative technologies in support of home-based, self-managed rehabilitation for stroke survivors. An intervention of daily walks can assist with improving lower limb motor function, and this can be measured by using technology. This paper focuses on assessing the usage of self-management technologies on poststroke survivors while undergoing rehabilitation at home. Methods: A realist evaluation of a personalized self-management rehabilitation system was undertaken in the homes of stroke survivors (N=5) over a period of approximately two months. Context, mechanisms, and outcomes were developed and explored using theories relating to motor recovery. Participants were encouraged to self-manage their daily walking activity; this was achieved through goal setting and motivational feedback. Gait data were collected and analyzed to produce metrics such as speed, heel strikes, and symmetry. This was achieved using a “smart insole” to facilitate measurement of walking activities in a free-living, nonrestrictive environment. Results: Initial findings indicated that 4 out of 5 participants performed better during the second half of the evaluation. Performance increase was evident through improved heel strikes on participants’ affected limb. Additionally, increase in performance in relation to speed was also evident for all 5 participants. A common strategy emerged across all but one participant as symmetry performance was sacrificed in favor of improved heel strikes. This paper evaluates compliance and intensity of use. Conclusion: Our findings suggested that 4 out of the 5 participants improved their ability to heel strike on their affected limb. All participants showed improvements in their speed of gait measured in steps per minute with an average increase of 9.8% during the rehabilitation program. Performance in relation to symmetry showed an 8.5% average decline across participants, although 1 participant improved by 4%. Context, mechanism, and outcomes indicated that dual motor learning and compensatory strategies were deployed by the participants

A unilateral robotic knee exoskeleton to assess the role of natural gait assistance in hemiparetic patients.

Background: Hemiparetic gait is characterized by strong asymmetries that can severely affect the quality of life of stroke survivors. This type of asymmetry is due to motor deficits in the paretic leg and the resulting compensations in the nonparetic limb. In this study, we aimed to evaluate the effect of actively promoting gait symmetry in hemiparetic patients by assessing the behavior of both paretic and nonparetic lower limbs. This paper introduces the design and validation of the REFLEX prototype, a unilateral active knee–ankle–foot orthosis designed and developed to naturally assist the paretic limbs of hemiparetic patients during gait. Methods: REFLEX uses an adaptive frequency oscillator to estimate the continuous gait phase of the nonparetic limb. Based on this estimation, the device synchronically assists the paretic leg following two different control strategies: (1) replicating the movement of the nonparetic leg or (2) inducing a healthy gait pattern for the paretic leg. Technical validation of the system was implemented on three healthy subjects, while the effect of the generated assistance was assessed in three stroke patients. The effects of this assistance were evaluated in terms of interlimb symmetry with respect to spatiotemporal gait parameters such as step length or time, as well as the similarity between the joint’s motion in both legs. Results: Preliminary results proved the feasibility of the REFLEX prototype to assist gait by reinforcing symmetry. They also pointed out that the assistance of the paretic leg resulted in a decrease in the compensatory strategies developed by the nonparetic limb to achieve a functional gait. Notably, better results were attained when the assistance was provided according to a standard healthy pattern, which initially might suppose a lower symmetry but enabled a healthier evolution of the motion of the nonparetic limb. Conclusions: This work presents the preliminary validation of the REFLEX prototype, a unilateral knee exoskeleton for gait assistance in hemiparetic patients. The experimental results indicate that assisting the paretic leg of a hemiparetic patient based on the movement of their nonparetic leg is a valuable strategy for reducing the compensatory mechanisms developed by the nonparetic limb.post-print6406 K

A new lower limb portable exoskeleton for gait assistance in neurological patients: a proof of concept study

Background: Few portable exoskeletons following the assist-as-needed concept have been developed for patients with neurological disorders. Thus, the main objectives of this proof-of-concept study were 1) to explore the safety and feasibility of an exoskeleton for gait rehabilitation in stroke and multiple sclerosis patients, 2) to test different algorithms for gait assistance and measure the resulting gait changes and 3) to evaluate the user's perception of the device. Methods: A cross-sectional study was conducted. Five patients were recruited (4 patients with stroke and 1 with multiple sclerosis). A robotic, one-degree-of-freedom, portable lower limb exoskeleton known as the Marsi Active Knee (MAK) was designed. Three control modes (the Zero Force Control mode, Mode 1 and Mode 3) were implemented. Spatiotemporal gait parameters were measured by the 10-m walking test (10MWT), the Gait Assessment and Intervention Tool (G.A.I.T.) and Tinetti Performance Oriented Mobility Assessment (gait subscale) before and after the trials. A modified QUEST 2.0 questionnaire was administered to determine each participant's opinion about the exoskeleton. The data acquired by the MAK sensors were normalized to a gait cycle, and adverse effects were recorded. Results: The MAK exoskeleton was used successfully without any adverse effects. Better outcomes were obtained in the 10MWT and G.A.I.T. when Mode 3 was applied compared with not wearing the device at all. In 2 participants, Mode 3 worsened the results. Additionally, Mode 3 seemed to improve the 10MWT and G.A.I.T. outcomes to a greater extent than Mode 1. The overall score for the user perception of the device was 2.8 ± 0.4 95% CI. Conclusions: The MAK exoskeleton seems to afford positive preliminary results regarding safety, feasibility, and user acceptance. The efficacy of the MAK should be studied in future studies, and more advanced improvements in safety must be implemented.G. Puyuelo-Quintana has received financial support by the “Doctorado Industrial” grant of Ministerio de Ciencia, Innovación y Universidades de España (reference DI-16- 08731). A. Plaza-Flores and E. Garces-Castellote have received financial support by the “Doctorado Industrial” grant of Comunidad de Madrid (reference IND2017/TIC-7698 and IND2018/TIC-9618, respectively).Peer reviewe

Somatosensory stimulation to improve lower-limb recovery after stroke

Introduction Increasing lower-limb sensation could improve walking post-stroke but evidence for this is limited. This thesis reports: 1) Review of published literature on somatosensory stimulation of the foot to enhance lower-limb function post-stroke. 2) Development of standardised intervention protocols for testing in a feasibility trial. 3) Feasibility trial of somatosensory stimulation interventions combined with functional activity. Methods 1) Systematic review with narrative synthesis of somatosensory stimulation to the foot to improve balance and gait post-stroke. 2) Modified Nominal Group Technique with experienced therapists, informed by literature, to develop and seek consensus on three standardised therapy protocols. a) lower-limb mobilization and tactile stimulation (MTS) b) textured insole wearing (TI) c) task-specific gait training (TSGT) 3) Mixed-methods, single-blind feasibility study explored: recruitment, participant characteristics, attrition, intervention and outcome measures acceptability (responses, feasibility, costs), sample size requirements, and participants’ experiences. Adults 42–112 days post-stroke were randomized to either TIs+TSGT or MTS+TSGT. Lower-limb sensorimotor and functional outcomes were measured pre-randomization, post-intervention, and one-month later. Participants’ experiences and acceptability of interventions and outcomes were explored in focus groups, with qualitative data analysed thematically. Quantitative feasibility outcomes were analysed using descriptive statistics, and within-group changes calculated. Results 1) Seventeen trials included in the review confirmed that evidence for somatosensory stimulation to improve lower-limb function post-stroke is limited. 2) Validated trial intervention protocols for MTS, TIs and TSGT were developed, with consensus. 3) Thirty-four stroke survivors were recruited and completed the trial, with acceptable recruitment (48.57%) and attrition (5.88%) rates. Feasibility of outcomes, costs, delivery and acceptability of interventions and outcome measures were confirmed. Potential response to treatment was noted. Conclusion Somatosensory stimulation of the foot post-stroke warrants investigation. Feasibility of a larger trial of somatosensory stimulation interventions was confirmed. Participant characteristics, response over time, and variance of outcome measures will inform a future larger trial

Développement et étude de la validité d'une semelle instrumentée pour le comptage de pas

Les semelles instrumentées sont des dispositifs pouvant être utilisées pour la quantification de pas et la reconnaissance des activités. Il existe plusieurs modèles de semelles instrumentées, avec des niveaux de validité variables. Ce mémoire comprend trois objectifs : 1) faire une revue systématique de la littérature sur la validité de critère des semelles instrumentées existantes pour identifier les postures, les types d’activités et compter les pas, 2) développer une semelle instrumentée et 3) étudier sa validité pour le comptage de pas. Pour l’objectif 1, cinq bases de données ont permis de sélectionner 33 articles sur la validité de critère de seize modèles de semelles instrumentées pour la détection de posture, de type d’activités et de pas. Selon les indicateurs utilisés, les validités de critère varient de 65,8% à 100% pour la reconnaissance des activités et des postures et de 96% à 100% pour la détection de pas. En somme, peu d’études ont utilisé les semelles instrumentées pour le comptage de pas bien qu’elles démontrent une très bonne validité. Pour les objectifs 2 et 3, nous avons équipé une semelle commercialisée de cinq capteurs de pression et testé trois méthodes de traitement des signaux de pression pour la quantification de pas. Ces trois méthodes sont basées sur le signal de chaque capteur de pression, la moyenne ou la somme cumulée des cinq signaux de pression. Les résultats ont montré que notre semelle instrumentée détectait le pas avec un taux de succès de 94,8 ± 9,4% à 99,5 ± 0,4% à des vitesses de marche confortable et de 97,0 ± 6,2% à 99,6 ± 0,4% à des vitesses de marche rapide à l’intérieur et à l’extérieur d’un bâtiment avec les trois méthodes. Toutefois, la méthode basée sur la somme cumulée avait les niveaux de précision plus élevés pour le comptage de pasInstrumented insoles are devices which can be used for quantifying steps and recognizing activities. Validity of many instrumented insoles varies from medium to high. This thesis has three objectives: 1) to systematically review the literature on the validity of existing instrumented insoles for posture, type of activities recognition, and step counting, 2) to develop an instrumented insole and 3) to study its criterion validity for step counting. For objective 1, five databases were used to select 33 articles on criterion validity of sixteen insole models for posture and type of activities recognition, and step detection. According to indicators used, validity values vary from 65.8% to 100% for activities and postures recognition and from 96% to 100% for detection of steps. In summary, few studies have used instrumented insoles for steps counting even though they demonstrated a very good validity. For objectives 2 and 3, we equipped a commercialized insole with five pressure sensors and tested three pressure signal processing methods for step quantification. These three methods are based on signal of each pressure sensor, average or cumulative sum of five pressure signals. Results showed that our instrumented insole detected steps with a success rate varying from 94.8 ± 9.4% to 99.5 ± 0.4% at self-selected walking speeds and from 97.0 ± 6.2% to 99.6 ± 0.4% at maximal walking speeds in indoor and outdoor settings with all three processing methods. However, cumulative sum method had the highest levels of accuracy for step counting

Wearables for Movement Analysis in Healthcare

Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients’ capabilities, unobtrusively and continuously, for clinical purposes

Instrumented shoes for daily activity monitoring in healthy and at risk populations

Daily activity reflects the health status of an individual. Ageing and disease drastically affect all dimensions of mobility, from the number of active bouts to their duration and intensity. Performing less activity leads to muscle deterioration and further weakness that could lead to increased fall risk. Gait performance is also affected by ageing and could be detrimental for daily mobility. Therefore, activity monitoring in older adults and at risk persons is crucial to obtain relevant quantitative information about daily life performance. Activity evaluation has mainly been established through questionnaires or daily logs. These methods are simple but not sufficiently accurate and are prone to errors. With the advent of microelectromechanical systems (MEMS), the availability of wearable sensors has shifted activity analysis towards ambulatory monitoring. In particular, inertial measurement units consisting of accelerometers and gyroscopes have shown to be extremely relevant for characterizing human movement. However, monitoring daily activity requires comfortable and easy to use systems that are strategically placed on the body or integrated in clothing to avoid movement hindrance. Several research based systems have employed multiple sensors placed at different locations, capable of recognizing activity types with high accuracy, but not comfortable for daily use. Single sensor systems have also been used but revealed inaccuracies in activity recognition. To this end, we propose an instrumented shoe system consisting of an inertial measurement unit and a pressure sensing insole with all the sensors placed at the shoe/foot level. By measuring the foot movement and loading, the recognition of locomotion and load bearing activities would be appropriate for activity classification. Furthermore, inertial measurement units placed on the foot can perform detailed gait analysis, providing the possibility of characterizing locomotion. The system and dedicated activity classification algorithms were first designed, tested and validated during the first part of the thesis. Their application to clinical rehabilitation of at risk persons was demonstrated over the second part. In the first part of the thesis, the designed instrumented shoes system was tested in standardized conditions with healthy elderly subjects performing a sequence of structured activities. An algorithm based on movement biomechanics was built to identify each activity, namely sitting, standing, level walking, stairs, ramps, and elevators. The rich array of sensors present in the system included a 3D accelerometer, 3D gyroscope, 8 force sensors, and a barometer allowing the algorithm to reach a high accuracy in classifying different activity types. The tuning parameters of the algorithm were shown to be robust to small changes, demonstrating the suitability of the algorithm to activity classification in older adults. Next, the system was tested in daily life conditions on the same elderly participants. Using a wearable reference system, the concurrent validity of the instrumented shoes in classifying daily activity was shown. Additionally, daily gait metrics were obtained and compared to the literature. Further insight into the relationship between some gait parameters as well as a global activity metric, the activity âcomplexityâ, was discussed. Participants positively rated their comfort while using the system... (Please refer to thesis for full abstract