Design and evaluation of a smart insole: Application for continuous monitoring of frail people at home

Charlon, Yoann; Campo, Éric; Brulin, Damien

doi:10.1016/j.eswa.2017.11.024

Cited by 23 publications

(15 citation statements)

References 35 publications

(56 reference statements)

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“…The Lilypad board is also interfaced through the I 2 C (Inter-Integrated Circuit) bus with three axes MMA8452Q (manufactured by NXP Semiconductors, Eindhoven, Netherlands) to count the number of steps performed and determine gait parameters ( Figure 21 ). Particularly, it is performed by extracting the time features of the acceleration trends provided by the accelerometers [ 45 , 54 ]. It is a low-energy three-axis MEMS capacitive accelerometer with 12-bit resolution, suitable embedded functions since featured by reduced dimensions and consumption, and flexible programming options configurable through two interrupt pins.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, to detect these gait events, a peak detection algorithm is implemented ( Figure 23 b) for determining gait parameters, i.e., the swing time (SWT) and stance phase time (SPT), expressed as:

where

and

are the instants of the toe detachment from the ground and heel support [ 45 , 46 , 55 , 56 ]. These parameters, properly monitored, can be used to detect or prevent the onset of any pathologies, such as the formation of diabetes ulcers [ 39 ].…”

Section: Resultsmentioning

confidence: 99%

“…Inertial sensors are also widely exploited to monitor the gait parameters quickly, reliably, and non-invasively. In particular, Y. Charlon et al presented a smart insole able to detect step count, covered distance, and gait speed [ 45 ]. Two prototypes were developed and tested in real use conditions; the first one included an MC13213 microcontroller to acquire data from ADXL345 accelerometer and A401 piezoresistive sensor ( Figure 9 a).…”

Section: An Overview Of Smart Insoles For Plantar Pressure Detection and Gait Analysismentioning

confidence: 99%

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Development of a Self-Powered Piezo-Resistive Smart Insole Equipped with Low-Power BLE Connectivity for Remote Gait Monitoring

Fazio

Perrone

Velázquez

et al. 2021

Sensors

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The evolution of low power electronics and the availability of new smart materials are opening new frontiers to develop wearable systems for medical applications, lifestyle monitoring, and performance detection. This paper presents the development and realization of a novel smart insole for monitoring the plantar pressure distribution and gait parameters; indeed, it includes a piezoresistive sensing matrix based on a Velostat layer for transducing applied pressure into an electric signal. At first, an accurate and complete characterization of Velostat-based pressure sensors is reported as a function of sizes, support material, and pressure trend. The realization and testing of a low-cost and reliable piezoresistive sensing matrix based on a sandwich structure are discussed. This last is interfaced with a low power conditioning and processing section based on an Arduino Lilypad board and an analog multiplexer for acquiring the pressure data. The insole includes a 3-axis capacitive accelerometer for detecting the gait parameters (swing time and stance phase time) featuring the walking. A Bluetooth Low Energy (BLE) 5.0 module is included for transmitting in real-time the acquired data toward a PC, tablet or smartphone, for displaying and processing them using a custom Processing® application. Moreover, the smart insole is equipped with a piezoelectric harvesting section for scavenging energy from walking. The onfield tests indicate that for a walking speed higher than 1 ms−1, the device’s power requirements (i.e., ) was fulfilled. However, more than 9 days of autonomy are guaranteed by the integrated 380-mAh Lipo battery in the total absence of energy contributions from the harvesting section.

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Section: Resultsmentioning

confidence: 99%

where

and

Section: Resultsmentioning

confidence: 99%

Section: An Overview Of Smart Insoles For Plantar Pressure Detection and Gait Analysismentioning

confidence: 99%

See 1 more Smart Citation

Development of a Self-Powered Piezo-Resistive Smart Insole Equipped with Low-Power BLE Connectivity for Remote Gait Monitoring

Fazio

Perrone

Velázquez

et al. 2021

Sensors

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“…The LAAS-CNRS (Laboratory for Analysis and Architecture of Systems-French National Centre for Scientific Research) has been developing systems for monitoring people since 1990 [87,88]. This mainly includes research on health monitoring for older adults and frail people at home based on a wireless local body area network (WLBAN) [89][90][91]. On the basis of the techniques and experiences of studying the WLBAN monitoring system for many years, our laboratory has started research on sleep monitoring.…”

Section: Our Proposition Of Sleep Monitoring Systemmentioning

confidence: 99%

Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review

Pan¹,

Brulin²,

Campo³

2020

JMIR Biomed Eng

Self Cite

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Background Sleep is essential for human health. Considerable effort has been put into academic and industrial research and in the development of wireless body area networks for sleep monitoring in terms of nonintrusiveness, portability, and autonomy. With the help of rapid advances in smart sensing and communication technologies, various sleep monitoring systems (hereafter, sleep monitoring systems) have been developed with advantages such as being low cost, accessible, discreet, contactless, unmanned, and suitable for long-term monitoring. Objective This paper aims to review current research in sleep monitoring to serve as a reference for researchers and to provide insights for future work. Specific selection criteria were chosen to include articles in which sleep monitoring systems or devices are covered. Methods This review investigates the use of various common sensors in the hardware implementation of current sleep monitoring systems as well as the types of parameters collected, their position in the body, the possible description of sleep phases, and the advantages and drawbacks. In addition, the data processing algorithms and software used in different studies on sleep monitoring systems and their results are presented. This review was not only limited to the study of laboratory research but also investigated the various popular commercial products available for sleep monitoring, presenting their characteristics, advantages, and disadvantages. In particular, we categorized existing research on sleep monitoring systems based on how the sensor is used, including the number and type of sensors, and the preferred position in the body. In addition to focusing on a specific system, issues concerning sleep monitoring systems such as privacy, economic, and social impact are also included. Finally, we presented an original sleep monitoring system solution developed in our laboratory. Results By retrieving a large number of articles and abstracts, we found that hotspot techniques such as big data, machine learning, artificial intelligence, and data mining have not been widely applied to the sleep monitoring research area. Accelerometers are the most commonly used sensor in sleep monitoring systems. Most commercial sleep monitoring products cannot provide performance evaluation based on gold standard polysomnography. Conclusions Combining hotspot techniques such as big data, machine learning, artificial intelligence, and data mining with sleep monitoring may be a promising research approach and will attract more researchers in the future. Balancing user acceptance and monitoring performance is the biggest challenge in sleep monitoring system research.

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“…Figure 13 shows real human plantar measurement photo and several displays of different foot pressure state with k value of 0.18. From this calibrated plantar pressure image and its digital values, our smart insole scheme is expected to be used for the human gait analysis if the wiring lines are replaced by a wireless communication device [17].…”

Section: Pressure Visualizationmentioning

confidence: 99%

Characterization of Elastic Polymer-Based Smart Insole and a Simple Foot Plantar Pressure Visualization Method Using 16 Electrodes

Lee

2018

Sensors

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In this paper, we propose a smart insole for inexpensive plantar pressure sensing and a simple visualizing scheme. The insole is composed of two elastomeric layers and two electrode layers where the common top electrode is submerged in the insole. The upper elastomeric layer is non-conductive poly-dimethyl-siloxane (PDMS) and supports plantar pressure buffering and the lower layer is carbon nano-tube (CNT)-dispersed PDMS for pressure sensing through piezo-resistivity. Under the lower sensing layer are 16 bottom electrodes for pressure distribution sensing without cell-to-cell interference. Since no soldering or sewing is needed the smart insole manufacturing processes is simple and cost-effective. The pressure sensitivity and time response of the material was measured and based on the 16 sensing data of the smart insole, we virtually extended the frame size for continuous and smoothed pressure distribution image with the help of a simple pseudo interpolation scheme.

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Design and evaluation of a smart insole: Application for continuous monitoring of frail people at home

Cited by 23 publications

References 35 publications

Development of a Self-Powered Piezo-Resistive Smart Insole Equipped with Low-Power BLE Connectivity for Remote Gait Monitoring

Development of a Self-Powered Piezo-Resistive Smart Insole Equipped with Low-Power BLE Connectivity for Remote Gait Monitoring

Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review

Characterization of Elastic Polymer-Based Smart Insole and a Simple Foot Plantar Pressure Visualization Method Using 16 Electrodes

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