Validity of the Loadsol Pro Insole for Pedal Reaction Force Measurements During Stationary Cycling

Advancements in wearable technology have allowed clinicians, coaches, and researchers the ability to observe and quantify human movement outside the laboratory. Instrumented insoles are an example of novel technology that can be worn in the shoes and measure vertical reaction force wirelessly. The use of such insoles will prove to be beneficial for athletes as they train, patients as they progress through rehabilitation, and researchers as they experiment in their respective fields. The Loadsol Pro (Novel Inc., St Paul., MN, USA) has been shown to produce accurate and reliable measures of ground reaction forces (GRF) in various dynamic activities including walking, running, and landing. However, the insoles have yet to be validated during bouts of stationary cycling. The standard for measuring forces during cycling is through instrumented bike pedals, yet such technology is costly, difficult to obtain, and requires extensive training. The purpose of the current study was to analyze the validity of the Loadsol Pro insole for pedal reaction force (PRF) measurements during stationary cycling. A total of 18 healthy subjects (age: 20.94 ± 2.24 years, weight: 72.4 ± 23.32 kg, height: 1.67 ± 0.06 m, body mass index: 25.72 ± 7.57 kg/m2) participated in the study. The Loadsol Pro insoles (200 Hz) and custom instrumented bike pedals (1200 Hz) were used to collect PRF data during bouts of stationary cycling at 50 W, 75 W, and 100 W. A paired samples t-test was performed to observe the agreement between both measurement systems and Cohen’s d effect size was calculated to indicate the effect of the observed differences. The paired samples t-test resulted in no statistically significant differences in peak PRF measured by the Loadsol and the instrumented pedals. Cohen’s d effect size resulted in small effect sizes between the Loadsol PRF and pedal PRF. Across all conditions, mean differences between the Loadsol PRF and pedal PRF were calculated to be less than 6 N with marginal errors under 4%. Thus, the Loadsol can be used to accurately measure peak PRF forces across work rates during stationary cycling. The introduction of the Loadsol to stationary cycling will provide easier access to data that is influential for health and in rehabilitative advances, and representative of athletic performance

Using plantar pressure for free-living posture recognition and sedentary behaviour monitoring

Health authorities in numerous countries and even the World Health Organization (WHO) are concerned with low levels of physical activity and increasing sedentary behaviour amongst the general population. In fact, emerging evidences identify sedentary behaviour as a ubiquitous characteristic of contemporary lifestyles. This has major implications for the general health of people worldwide particularly for the prevalence of non-communicable conditions (NCDs) such as cardiovascular disease, diabetes and cancer and their risk factors such as raised blood pressure, raised blood sugar and overweight. Moreover, sedentary time appears to be uniquely associated with health risks independent of physical activity intensity levels. However, habitual sedentary behaviour may prove complex to be accurately measured as it occurs across different domains, including work, transport, domestic duties and even lei¬sure. Since sedentary behaviour is mostly reflect as too much sitting, one of the main concerns is being able to distinguish among different activities, such as sitting and standing. Widely used devices such as accelerometer-based activity monitors have a limited ability to detect sedentary activities accurately. Thus, there is a need of a viable large-scale method to efficiently monitor sedentary behaviour. This thesis proposes and demonstrates how a plantar pressure based wearable device and machine learning classification techniques have significant capability to monitor daily life sedentary behaviour. Firstly, an in-depth review of research and market ready plantar pressure and force technologies is performed to assess their measurement capabilities and limitations to measure sedentary behaviour. Afterwards, a novel methodology for measuring daily life sedentary behaviour using plantar pressure data and a machine learning predictive model is developed. The proposed model and its algorithm are constructed using a dataset of 20 participants collected at both laboratory-based and free-living conditions. Sitting and standing variations are included in the analysis as well as the addition of a potential novel activities, such as leaning. Video footage is continuously collected using of a wearable camera as an equivalent of direct observation to allow the labelling of the training data for the machine learning model. The optimal parameters of the model such as feature set, epoch length, type of classifier is determined by experimenting with multiple iterations. Different number and location of plantar pressure sensors are explored to determine the optimal trade-off between low computational cost and accurate performance. The model s performance is calculated using both subject dependent and subject independent validation by performing 10-fold stratified cross-validation and leave-one-user-out validation respectively. Furthermore, the proposed model activity performance for daily life monitoring is validated against the current criterion (i.e. direct observation) and against the de facto standard, the activPAL. The results show that the proposed machine learning classification model exhibits excel-lent recall rates of 98.83% with subject dependent training and 95.93% with independent training. This work sets the groundwork for developing a future plantar pressure wearable device for daily life sedentary behaviour monitoring in free-living conditions that uses the proposed ma-chine leaning classification model. Moreover, this research also considers important design characteristics of wearable devices such as low computational cost and improved performance, addressing the current gap in the physical activity and sedentary behaviour wearable market

Sensores de fibra ótica para arquiteturas e-Health

In this work, optical fiber sensors were developed and optimized for biomedical applications in wearable and non-intrusive and/or invisible solutions. As it was intended that the developed devices would not interfere with the user's movements and their daily life, the fibre optic sensors presented several advantages when compared to conventional electronic sensors, among others, the following stand out: size and reduced weight, biocompatibility, safety, immunity to electromagnetic interference and high sensitivity. In a first step, wearable devices with fibre optic sensors based in Fiber Bragg gratings (FBG) were developed to be incorporated into insoles to monitor different walking parameters based on the analysis of the pressure exerted on several areas of the insole. Still within this theme, other sensors were developed using the same sensing technology, but capable of monitoring pressure and shear forces simultaneously. This work was pioneering and allowed monitoring one of the main causes of foot ulceration in people with diabetes: shear. At a later stage, the study focused on the issue related with the appearance of ulcers in people with reduced mobility and wheelchair users. In order to contribute to the mitigation of this scourge, a system was developed composed of a network of fibre optic sensors capable of monitoring the pressure at various points of the wheelchair. It not only measures the pressure at each point, but also monitors the posture of the wheelchair user and advises him/her to change posture regularly to reduce the probability of this pathology occurring. Still within this application, another work was developed where the sensor not only monitored the pressure but also the temperature in each of the analysis points, thus indirectly measuring shear. In another phase, plastic fibre optic sensors were studied and developed to monitor the body posture of an office chair user. Simultaneously, software was developed capable of monitoring and showing the user all the acquired data in real time and warning for incorrect postures, as well as advising for work breaks. In a fourth phase, the study focused on the development of highly sensitive sensors embedded in materials printed by a 3D printer. The sensor was composed of an optical fibre with a FBG and the sensor body of a flexible polymeric material called "Flexible". This material was printed on a 3D printer and during its printing the optical fibre was incorporated. The sensor proved to be highly sensitive and was able to monitor respiratory and cardiac rate, both in wearable solutions (chest and wrist) and in "invisible" solutions (office chair).Neste trabalho foram desenvolvidos e otimizados sensores em fibra ótica para aplicações biomédicas em soluções vestíveis e não intrusivas/ou invisíveis. Tendo em conta que se pretende que os dispositivos desenvolvidos não interfiram com os movimentos e o dia-a-dia do utilizador, os sensores de fibra ótica apresentam inúmeras vantagens quando comparados com os sensores eletrónicos convencionais, de entre várias, destacam-se: tamanho e peso reduzido, biocompatibilidade, segurança, imunidade a interferências eletromagnéticas e elevada sensibilidade. Numa primeira etapa, foram desenvolvidos dispositivos vestíveis com sensores de fibra ótica baseados em redes de Bragg (FBG) para incorporar em palmilhas de modo a monitorizar diferentes parâmetros da marcha com base na análise da pressão exercida em várias zonas da palmilha. Ainda no âmbito deste tema, adicionalmente, foram desenvolvidos sensores utilizando a mesma tecnologia de sensoriamento, mas capazes de monitorizar simultaneamente pressão e forças de cisalhamento. Este trabalho foi pioneiro e permitiu monitorizar um dos principais responsáveis pela ulceração dos pés em pessoas com diabetes: o cisalhamento. Numa fase posterior, o estudo centrou-se na temática relacionada com o aparecimento de úlceras em pessoas com mobilidade reduzida e utilizadores de cadeiras de rodas. De modo a contribuir para a mitigação deste flagelo, procurou-se desenvolver um sistema composto por uma rede de sensores de fibra ótica capaz de monitorizar a pressão em vários pontos de uma cadeira de rodas e não só aferir a pressão em cada ponto, mas monitorizar a postura do cadeirante e aconselhá-lo a mudar de postura com regularidade, de modo a diminuir a probabilidade de ocorrência desta patologia. Ainda dentro desta aplicação, foi publicado um outro trabalho onde o sensor não só monitoriza a pressão como também a temperatura em cada um dos pontos de análise, conseguindo aferir assim indiretamente o cisalhamento. Numa outra fase, foi realizado o estudo e desenvolvimento de sensores de fibra ótica de plástico para monitorizar a postura corporal de um utilizador de uma cadeira de escritório. Simultaneamente, foi desenvolvido um software capaz de monitorizar e mostrar ao utilizador todos os dados adquiridos em tempo real e advertir o utilizador de posturas incorretas, bem como aconselhar para pausas no trabalho. Numa quarta fase, o estudo centrou-se no desenvolvimento de sensores altamente sensíveis embebidos em materiais impressos 3D. O sensor é composto por uma fibra ótica com uma FBG e o corpo do sensor por um material polimérico flexível, denominado “Flexible”. O sensor foi impresso numa impressora 3D e durante a sua impressão foi incorporada a fibra ótica. O sensor demonstrou ser altamente sensível e foi capaz de monitorizar frequência respiratória e cardíaca, tanto em soluções vestíveis (peito e pulso) como em soluções “invisíveis” (cadeira de escritório).Programa Doutoral em Engenharia Físic

Foot Motion-Based Falling Risk Evaluation for Patients with Parkinson’s Disease

Parkinson’s disease (PD) affects motor functionalities, which are closely associated with increased risks of falling and decreased quality of life. However, there is no easy-to-use definitive tools for PD patients to quantify their falling risks at home. To address this, in this dissertation, we develop Monitoring Insoles (MONI) with advanced data processing techniques to score falling risks of PD patients following Falling Risk Questionnaire (FRQ) developed by the U.S. Centers for Disease Control and Prevention (CDC). To achieve this, we extract motion tasks from daily activities and select the most representative features associated with PD that facilitate accurate falling risk scoring. To address the challenge in uncontrolled daily life environments and to identify the most representative features associated with PD and falling risks, the proposed data processing method firstly recognizes foot motions such as walking and toe tapping from continuous movements with stride detection and fast labeling framework, and then extracts time-axis and acceleration-axis features from the motion tasks, at the end provides a score of falling risks using regression. The data processing method can be integrated into a mobile game to be used at home with MONI. The main contributions of this dissertation includes: (i) developing MONI as a low power solution for daily life use; (ii) utilizing stride detection and developing fast labeling framework for motion recognition that improves recognition accuracy for daily life applications; (iii) analyzing two walking and two toe tapping tasks that are close to real life scenarios and identifying important features associated with PD and falling risks; (iv) providing falling scores as quantitative evaluation to PD patients in daily life through simple foot motion tasks and setups

Electronic design for a fully wireless smart insole

Aquesta tesi representa el disseny d'una plantilla dissenyada per detectar caigudes i monitoritzar i caracteritzar els passos al caminar amb l'objectiu de minimitzar el risc de caigudes, especialment en gent gran. La plantilla és prou prima com per ser comfortable per a l'usuari i no te cap contacte elèctric. El prototip incorpora tecnologies avançades com Bluetooth 5, plaques de circuit imprès flexi-rígides, sensors MEMS i tèxtils, càrrega per inducció i interruptors magnètics. Aquestes característiques permeten que la plantilla funcioni en una àmplia varietat d'entorns sense el risc d'un mal funcionament a causa de la humitat o la suor

Low-Cost Sensors and Biological Signals

Many sensors are currently available at prices lower than USD 100 and cover a wide range of biological signals: motion, muscle activity, heart rate, etc. Such low-cost sensors have metrological features allowing them to be used in everyday life and clinical applications, where gold-standard material is both too expensive and time-consuming to be used. The selected papers present current applications of low-cost sensors in domains such as physiotherapy, rehabilitation, and affective technologies. The results cover various aspects of low-cost sensor technology from hardware design to software optimization

Prediction and Detection of Freezing of Gait in Parkinson's Disease using Plantar Pressure Data

Parkinson’s disease (PD) is a progressive neurodegenerative disorder affecting movement and is characterized by symptoms such as tremor, rigidity, and Freezing of Gait (FOG). FOG is a walking disturbance seen in more advanced stages of PD. FOG is characterized by the feeling of feet being glued to the ground and has been associated with higher risks of falls. While falling can have great repercussions in individuals with PD, leading to restricted movement and independence, hip fracture, and fatal injury, even the disturbance of FOG alone can lead to decreased mobility, inactivity, and decreased quality of life. Determining methods to counter FOG can potentially lead to a better life for people with PD (PwPD). Freezing episodes can be countered with the help of external intervention such as visual or auditory cues. Such intervention when administered during the freeze has been found to alleviate the freeze and thus prevent freeze-related falls. This sheds the importance of detecting or predicting a freeze event. Once a freeze is detected or predicted, an intervention can be administered to help prevent the freeze altogether (in case of prediction) or help resume normal walking (in case of detection). Different wearable sensors have been used to collect data from participants to understand FOG and develop approaches to detect and predict it. Plantar pressure data has earlier been used in gait related studies; however, they have not been used for FOG detection or prediction. Based on the hypothesis that plantar pressure data can capture subtle weight shifts unique to FOG episodes, this research aimed to determine if plantar pressure data alone can be used to detect and predict FOG. In this research, plantar (foot sole) pressure data were collected from shoe-insole sensors worn by 11 participants with PD as they walked a predefined freeze-provoking path while on their normal antiparkinsonian medication. The sensors included IMU, EMG, and plantar pressure foot insoles; however, for the research in this thesis, only plantar pressure data were used. The walking trials were also video recorded for labelling the data. A custom-built application was used to synchronize data from all sensors and label them. This was followed by feature extraction, dataset balancing, and z-score normalization. The datasets generated were then classified using Long-short term memory (LSTM) networks. The best model had an average 82.0% (SD 6.25%) sensitivity and 89.4% (SD 3.60%) specificity for one-freezer-held-out cross validation tests. For the participants who did not freeze during the walking trials, an average 87.7% specificity was achieved. Since, FOG detection is done with the aim to provide an intervention, a freeze episode analysis was completed, and it was found that the model could correctly detect 95% of freeze episodes. The misclassified freezes and false positives were analyzed with respect to active (walking and turning) and inactive states (standing). The model’s specificity performance for one-freezer held out cross validation tests was found to improve to 93.3% when analyzing the model only on active states. FOG prediction was done afterwards, including data before FOG (labelled Pre-FOG) in the target class. The best FOG prediction method achieved an average 74.02% (SD 12.48%) sensitivity and 82.99% (SD 5.75%) specificity for one-freezer-held-out cross validation tests. The research showed that plantar pressure data can be successfully used for FOG detection and prediction. Moving away from window-based model also helped the research in reducing the freeze detection latency. However, further research is required to improve the FOG prediction performance and a bigger sample size should be used in future research

Proceedings XXI Congresso SIAMOC 2021

XXI Congresso Annuale della SIAMOC, modalità telematica il 30 settembre e il 1° ottobre 2021. Come da tradizione, il congresso vuole essere un’occasione di arricchimento e mutuo scambio, dal punto di vista scientifico e umano. Verranno toccati i temi classici dell’analisi del movimento, come lo sviluppo e l’applicazione di metodi per lo studio del movimento nel contesto clinico, e temi invece estremamente attuali, come la teleriabilitazione e il telemonitoraggio