scholarly journals Deep learning approach to estimate foot pressure distribution in walking with application for a cost-effective insole system

Author(s):  
Frederick Mun ◽  
Ahnryul Choi

Abstract Background Foot pressure distribution can be used as a quantitative parameter for evaluating anatomical deformity of the foot and for diagnosing and treating pathological gait, falling, and pressure sores in diabetes. The objective of this study was to propose a deep learning model that could predict pressure distribution of the whole foot based on information obtained from a small number of pressure sensors in an insole. Methods Twenty young and twenty older adults walked a straight pathway at a preferred speed with a Pedar-X system in anti-skid socks. A long short-term memory (LSTM) model was used to predict foot pressure distribution. Pressure values of nine major sensors and the remaining 90 sensors in a Pedar-X system were used as input and output for the model, respectively. The performance of the proposed LSTM structure was compared with that of a traditionally used adaptive neuro-fuzzy interference system (ANFIS). A low-cost insole system consisting of a small number of pressure sensors was fabricated. A gait experiment was additionally performed with five young and five older adults, excluding subjects who were used to construct models. The Pedar-X system placed parallelly on top of the insole prototype developed in this study was in anti-skid socks. Sensor values from a low-cost insole prototype were used as input of the LSTM model. The accuracy of the model was evaluated by applying a leave-one-out cross-validation. Results Correlation coefficient and relative root mean square error (RMSE) of the LSTM model were 0.98 (0.92 ~ 0.99) and 7.9 ± 2.3%, respectively, higher than those of the ANFIS model. Additionally, the usefulness of the proposed LSTM model for fabricating a low-cost insole prototype with a small number of sensors was confirmed, showing a correlation coefficient of 0.63 to 0.97 and a relative RMSE of 12.7 ± 7.4%. Conclusions This model can be used as an algorithm to develop a low-cost portable smart insole system to monitor age-related physiological and anatomical alterations in foot. This model has the potential to evaluate clinical rehabilitation status of patients with pathological gait, falling, and various foot pathologies when more data of patients with various diseases are accumulated for training.

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6082
Author(s):  
Dhivakar Rajendran ◽  
Rajarajan Ramalingame ◽  
Saravanan Palaniyappan ◽  
Guntram Wagner ◽  
Olfa Kanoun

Foot pressure measurement plays an essential role in healthcare applications, clinical rehabilitation, sports training and pedestrian navigation. Among various foot pressure measurement techniques, in-shoe sensors are flexible and can measure the pressure distribution accurately. In this paper, we describe the design and characterization of flexible and low-cost multi-walled carbon nanotubes (MWCNT)/Polydimethylsiloxane (PDMS) based pressure sensors for foot pressure monitoring. The sensors have excellent electrical and mechanical properties an show a stable response at constant pressure loadings for over 5000 cycles. They have a high sensitivity of 4.4 kΩ/kPa and the hysteresis effect corresponds to an energy loss of less than 1.7%. The measurement deviation is of maximally 0.13% relative to the maximal relative resistance. The sensors have a measurement range of up to 330 kPa. The experimental investigations show that the sensors have repeatable responses at different pressure loading rates (5 N/s to 50 N/s). In this paper, we focus on the demonstration of the functionality of an in-sole based on MWCNT/PDMS nanocomposite pressure sensors, weighing approx. 9.46 g, by investigating the foot pressure distribution while walking and standing. The foot pressure distribution was investigated by measuring the resistance changes of the pressure sensors for a person while walking and standing. The results show that pressure distribution is higher in the forefoot and the heel while standing in a normal position. The foot pressure distribution is transferred from the heel to the entire foot and further transferred to the forefoot during the first instance of the gait cycle.


Author(s):  
Darryl G. Humphrey ◽  
Arthur F. Kramer ◽  
Sheryl S. Gore

Older adults have evidenced a poorer ability to use grouping factors in such tasks as Embedded Figures, Incomplete Figures, and partial report. Difficulties in disambiguating the findings of these studies has left unanswered the cause of this age-related difference. By taking into account age-related differences in visual short-term memory, the results of the current study suggest that older adults maintain the ability to capitalize on the perceptual organization of the visual environment as a means of facilitating recall performance. These results have implications for the design of information displays, product labels, codes, and instructions.


2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S298-S298
Author(s):  
Linda Roberts ◽  
Charles Cornell ◽  
Mathias Bostrom ◽  
Sandra Goldsmith ◽  
Titilayo Ologhobo ◽  
...  

Abstract Older adults often perceive themselves as stigmatized and powerless in healthcare settings. Communication with them is complicated by age-related issues and negative stereotypes about older adults and aging. It is therefore vital for physicians and surgeons, who encounter the most vulnerable elderly, to communicate successfully with this population, who wish to maintain quality and dignity in their lives. Successful patient communication leads to better recall of information, compliance, adherence to medications, satisfaction, and overall better outcomes. We developed a two-part training program comprised of small group interactive didactic sessions on aging issues with third year surgical residents, and workshop demonstrations given by the residents to a group of older adults, followed by a question and answer session. Residents were assessed using a 22-item pre–post questionnaire covering medical knowledge of aging, attitudes toward older adults, and personal anxiety about aging. Since its inception, the program has reached 88 residents and 711 older adults. For residents, knowledge scores (p ≤ 0.001), six of nine attitude items (p ≤ 0.01) and one of four anxiety items (p ≤ 0.001) improved significantly. This is notable as well since attitudes and anxiety levels are attributes that are deep-seated and hard to change. For older adults, post surveys showed that 96% strongly agreed/agreed that residents had demonstrated sensitivity toward them and 96% were very satisfied/satisfied with the program. Our replicable, low-cost program enables residents to learn and realistically practice universal underlying communication skills in order to maintain effective and sensitive communication with this vulnerable population.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Seçkin Arslan ◽  
Katerina Palasis ◽  
Fanny Meunier

Abstract This study reports on an event-related potentials experiment to uncover whether per-millisecond electrophysiological brain activity and analogous behavioural responses are age-sensitive when comprehending anaphoric (referent-first) and cataphoric (pronoun-first) pronouns. Two groups of French speakers were recruited (young n = 18; aged 19–35 and older adults n = 15; aged 57–88) to read sentences where the anaphoric/cataphoric pronouns and their potential referents either matched or mismatched in gender. Our findings indicate that (1) the older adults were not less accurate or slower in their behavioural responses to the mismatches than the younger adults, (2) both anaphoric and cataphoric conditions evoked a central/parietally distributed P600 component with similar timing and amplitude in both the groups. Importantly, mean amplitudes of the P600 effect were modulated by verbal short-term memory span in the older adults but not in the younger adults, (3) nevertheless, the older but not the younger adults displayed an additional anterior negativity emerging on the frontal regions in response to the anaphoric mismatches. These results suggest that pronoun processing is resilient in healthy ageing individuals, but that functional recruitment of additional brain regions, evidenced with the anterior negativity, compensates for increased processing demands in the older adults’ anaphora processing.


1978 ◽  
Vol 46 (2) ◽  
pp. 571-576 ◽  
Author(s):  
J. K. Adamowicz

Visual short-term memory of young and older adults was studied in relation to imaging ability. Both recall and recognition memory tasks were used and additional variables included stimulus complexity and response delay (recognition tasks) and stimulus complexity and visual masking (recall tasks). Young and older participants were matched on visual discrimination, verbal intelligence, and imaging ability. Stimuli consisted of abstract visual patterns. Age-related decrements in recognition and recall were observed but performance was related to imaging ability only with recall tasks and only for older adults. The results were discussed with reference to mediational strategies and locus of occurrence of age-related decrements in short-term memory.


2021 ◽  
Vol 11 (24) ◽  
pp. 12059
Author(s):  
Giulio Siracusano ◽  
Francesca Garescì ◽  
Giovanni Finocchio ◽  
Riccardo Tomasello ◽  
Francesco Lamonaca ◽  
...  

In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven structural health monitoring (SHM) systems is gaining in popularity. This is due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as deep learning. A promising method suitable for smart SHM is the analysis of acoustic emissions (AEs), i.e., ultrasonic waves generated by internal ruptures of the concrete when it is stressed. The advantage in respect to traditional ultrasonic measurement methods is the absence of the emitter and the suitability to implement continuous monitoring. The main purpose of this paper is to combine deep neural networks with bidirectional long short term memory and advanced statistical analysis involving instantaneous frequency and spectral kurtosis to develop an accurate classification tool for tensile, shear and mixed modes originated from AE events (cracks). We investigated effective event descriptors to capture the unique characteristics from the different types of modes. Tests on experimental results confirm that this method achieves promising classification among different crack events and can impact on the design of the future of SHM technologies. This approach is effective to classify incipient damages with 92% of accuracy, which is advantageous to plan maintenance.


2020 ◽  
Author(s):  
Yu Tian ◽  
Gaofeng pan ◽  
Mohamed-Slim Alouini

<div>Deep learning (DL) has seen great success in the computer vision (CV) field, and related techniques have been used in security, healthcare, remote sensing, and many other fields. As a parallel development, visual data has become universal in daily life, easily generated by ubiquitous low-cost cameras. Therefore, exploring DL-based CV may yield useful information about objects, such as their number, locations, distribution, motion, etc. Intuitively, DL-based CV can also facilitate and improve the designs of wireless communications, especially in dynamic network scenarios. However, so far, such work is rare in the literature. The primary purpose of this article, then, is to introduce ideas about applying DL-based CV in wireless communications to bring some novel degrees of freedom to both theoretical research and engineering applications. To illustrate how DL-based CV can be applied in wireless communications, an example of using a DL-based CV with a millimeter-wave (mmWave) system is given to realize optimal mmWave multiple-input and multiple-output (MIMO) beamforming in mobile scenarios. In this example, we propose a framework to predict future beam indices from previously observed beam indices and images of street views using ResNet, 3-dimensional ResNext, and a long short-term memory network. The experimental results show that our frameworks achieve much higher accuracy than the baseline method, and that visual data can significantly improve the performance of the MIMO beamforming system. Finally, we discuss the opportunities and challenges of applying DL-based CV in wireless communications.</div>


2021 ◽  
Vol 15 ◽  
Author(s):  
Charly G. Lecomte ◽  
Johannie Audet ◽  
Jonathan Harnie ◽  
Alain Frigon

Gait analysis in cats and other animals is generally performed with custom-made or commercially developed software to track reflective markers placed on bony landmarks. This often involves costly motion tracking systems. However, deep learning, and in particular DeepLabCutTM (DLC), allows motion tracking without requiring placing reflective markers or an expensive system. The purpose of this study was to validate the accuracy of DLC for gait analysis in the adult cat by comparing results obtained with DLC and a custom-made software (Expresso) that has been used in several cat studies. Four intact adult cats performed tied-belt (both belts at same speed) and split-belt (belts operating at different speeds) locomotion at different speeds and left-right speed differences on a split-belt treadmill. We calculated several kinematic variables, such as step/stride lengths and joint angles from the estimates made by the two software and assessed the agreement between the two measurements using intraclass correlation coefficient or Lin’s concordance correlation coefficient as well as Pearson’s correlation coefficients. The results showed that DLC is at least as precise as Expresso with good to excellent agreement for all variables. Indeed, all 12 variables showed an agreement above 0.75, considered good, while nine showed an agreement above 0.9, considered excellent. Therefore, deep learning, specifically DLC, is valid for measuring kinematic variables during locomotion in cats, without requiring reflective markers and using a relatively low-cost system.


2020 ◽  
Author(s):  
Yu Tian ◽  
Gaofeng pan ◽  
Mohamed-Slim Alouini

<div>Deep learning (DL) has seen great success in the computer vision (CV) field, and related techniques have been used in security, healthcare, remote sensing, and many other fields. As a parallel development, visual data has become universal in daily life, easily generated by ubiquitous low-cost cameras. Therefore, exploring DL-based CV may yield useful information about objects, such as their number, locations, distribution, motion, etc. Intuitively, DL-based CV can also facilitate and improve the designs of wireless communications, especially in dynamic network scenarios. However, so far, such work is rare in the literature. The primary purpose of this article, then, is to introduce ideas about applying DL-based CV in wireless communications to bring some novel degrees of freedom to both theoretical research and engineering applications. To illustrate how DL-based CV can be applied in wireless communications, an example of using a DL-based CV with a millimeter-wave (mmWave) system is given to realize optimal mmWave multiple-input and multiple-output (MIMO) beamforming in mobile scenarios. In this example, we propose a framework to predict future beam indices from previously observed beam indices and images of street views using ResNet, 3-dimensional ResNext, and a long short-term memory network. The experimental results show that our frameworks achieve much higher accuracy than the baseline method, and that visual data can significantly improve the performance of the MIMO beamforming system. Finally, we discuss the opportunities and challenges of applying DL-based CV in wireless communications.</div>


2020 ◽  
Author(s):  
Yu Tian ◽  
Gaofeng pan ◽  
Mohamed-Slim Alouini

<div>Deep learning (DL) has obtained great success in computer vision (CV) field, and the related techniques have been widely used in security, healthcare, remote sensing, etc. On the other hand, visual data is universal in our daily life, which is easily generated by prevailing but low-cost cameras. Therefore, DL-based CV can be explored to obtain and forecast some useful information about the objects, e.g., the number, locations, distribution, motion, etc. Intuitively, DL-based CV can facilitate and improve the designs of wireless communications, especially in dynamic network scenarios. However, so far, it is rare to see such kind of works in the existing literature. Then, the primary purpose of this article is to introduce ideas of applying DL-based CV in wireless communications to bring some novel degrees of freedom for both theoretical researches and engineering applications. To illustrate how DL-based CV can be applied in wireless communications, an example of using DL-based CV to millimeter wave (mmWave) system is given to realize optimal mmWave multiple-input and multiple-output (MIMO) beamforming in mobile scenarios. In this example, we proposed a framework to predict the future beam indices from the previously-observed beam indices and images of street views by using ResNet, 3-dimensional ResNext, and long short term memory network. Experimental results show that our frameworks can achieve much higher accuracy than the baseline method, and visual data can help significantly improve the performance of MIMO beamforming system. Finally, we discuss the opportunities and challenges of applying DL-based CV in wireless communications.</div>


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