RESUMEN
Smart shoes have ushered in a new era of personalised health monitoring and assistive technologies. Smart shoes leverage technologies such as Bluetooth for data collection and wireless transmission, and incorporate features such as GPS tracking, obstacle detection, and fitness tracking. As the 2010s unfolded, the smart shoe landscape diversified and advanced rapidly, driven by sensor technology enhancements and smartphones' ubiquity. Shoes have begun incorporating accelerometers, gyroscopes, and pressure sensors, significantly improving the accuracy of data collection and enabling functionalities such as gait analysis. The healthcare sector has recognised the potential of smart shoes, leading to innovations such as shoes designed to monitor diabetic foot ulcers, track rehabilitation progress, and detect falls among older people, thus expanding their application beyond fitness into medical monitoring. This article provides an overview of the current state of smart shoe technology, highlighting the integration of advanced sensors for health monitoring, energy harvesting, assistive features for the visually impaired, and deep learning for data analysis. This study discusses the potential of smart footwear in medical applications, particularly for patients with diabetes, and the ongoing research in this field. Current footwear challenges are also discussed, including complex construction, poor fit, comfort, and high cost.
Asunto(s)
Zapatos , Humanos , Teléfono Inteligente , Encuestas y Cuestionarios , Dispositivos Electrónicos Vestibles , Acelerometría/instrumentación , Pie Diabético/rehabilitación , Pie Diabético/prevención & control , Monitoreo Ambulatorio/métodos , Monitoreo Ambulatorio/instrumentación , Monitoreo Fisiológico/instrumentación , Monitoreo Fisiológico/métodos , Marcha/fisiologíaRESUMEN
BACKGROUND: Diabetic foot ulceration is a serious challenge worldwide which imposes an immense risk of lower extremity amputation and in many cases may lead to the death. The presented work focuses on the offloading requirements using an active approach and considers the use of magnetorheological fluid-based modules to redistribute high plantar pressures (PPs). METHODS & RESULTS: Experimentation validated a single module with a threshold peak pressure of 450 kPa, whereas an offloading test with a three-module array and complete footwear validated a maximum pressure reduction of 42.5% and 34.6%, respectively. CONCLUSION: To our knowledge, no such active and electrically controllable offloading footwear has been reported yet that has experimentally demonstrated PP reduction of more than 30% over the offloading site.
RESUMEN
The increase in the proportion of elderly in Europe brings with it certain challenges that society needs to address, such as custodial care. We propose a scalable, easily modulated and live assistive technology system, based on a comfortable smart footwear capable of detecting walking behaviour, in order to prevent possible health problems in the elderly, facilitating their urban life as independently and safety as possible. This brings with it the challenge of handling the large amounts of data generated, transmitting and pre-processing that information and analysing it with the aim of obtaining useful information in real/near-real time. This is the basis of information theory. This work presents a complete system aiming at elderly people that can detect different user behaviours/events (sitting, standing without imbalance, standing with imbalance, walking, running, tripping) through information acquired from 20 types of sensor measurements (16 piezoelectric pressure sensors, one accelerometer returning reading for the 3 axis and one temperature sensor) and warn the relatives about possible risks in near-real time. For the detection of these events, a hierarchical structure of cascading binary models is designed and applied using artificial neural network (ANN) algorithms and deep learning techniques. The best models are achieved with convolutional layered ANN and multilayer perceptrons. The overall event detection performance achieves an average accuracy and area under the ROC curve of 0.84 and 0.96, respectively.
RESUMEN
Technological advancements in wearable devices have revolutionized smart shoes. Smart shoes are sometimes referred to as intelligent shoes or computer-based shoes. They are capable of recognizing and recording data from day-to-day activities by the user. Such smart shoes are designed with sensors, vibrating motors, GPS, wireless systems, and various other sensors/actuators for the comfort and benefit of the wearer. In the current manuscript, we are reviewing various technologies that are implemented in smart shoes.
Asunto(s)
Zapatos , Dispositivos Electrónicos Vestibles , HumanosRESUMEN
Frailty assessment is dependent on the availability of trained personnel and it is currently limited to clinic and supervised setting. The growing aging population has made it necessary to find phenotypes of frailty that can be measured in an unsupervised setting for translational application in continuous, remote, and in-place monitoring during daily living activity, such as walking. We analyzed gait performance of 161 older adults using a shin-worn inertial sensor to investigate the feasibility of developing a foot-worn sensor to assess frailty. Sensor-derived gait parameters were extracted and modeled to distinguish different frailty stages, including non-frail, pre-frail, and frail, as determined by Fried Criteria. An artificial neural network model was implemented to evaluate the accuracy of an algorithm using a proposed set of gait parameters in predicting frailty stages. Changes in discriminating power was compared between sensor data extracted from the left and right shin sensor. The aim was to investigate the feasibility of developing a foot-worn sensor to assess frailty. The results yielded a highly accurate model in predicting frailty stages, irrespective of sensor location. The independent predictors of frailty stages were propulsion duration and acceleration, heel-off and toe-off speed, mid stance and mid swing speed, and speed norm. The proposed model enables discriminating different frailty stages with area under curve ranging between 83.2â»95.8%. Furthermore, results from the neural network suggest the potential of developing a single-shin worn sensor that would be ideal for unsupervised application and footwear integration for continuous monitoring during walking.