ArticleElectronics2014
SmartStep: A Fully Integrated, Low-Power Insole Monitor.
Article in Electronics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Wearable Sensors and IoT-Based Monitoring Applications for Older Adults - a Focus on Ageing Population and Independent Living.Journal of medical systems · 2019Pooled it
- Falls Detection and Prevention Systems in Home Care for Older Adults: Myth or Reality?JMIR aging · 2021Article
- Evaluation and Application of a Customizable Wireless Platform: A Body Sensor Network for Unobtrusive Gait Analysis in Everyday Life.Sensors (Basel, Switzerland) · 2020Article
- Data Transmission Efficiency in Bluetooth Low Energy Versions.Sensors (Basel, Switzerland) · 2019Article
- Automatic Recognition of Activities of Daily Living Utilizing Insole-Based and Wrist-Worn Wearable Sensors.IEEE journal of biomedical and health informatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Shoe-mounted wearable sensors can be used in applications, such as activity monitoring, gait analysis, post-stroke rehabilitation, body weight measurements and energy expenditure studies. Such wearable sensors typically require the modification or alteration of the shoe, which is not typically feasible for large populations without the direct involvement of shoe manufacturers. This article presents an insole-based wearable sensor (SmartStep) that has its electronics fully embedded into a generic insole, which is usable with a large variety of shoes and, thus, resolves the need for shoe modification. The SmartStep is an always-on electronic device that comprises a 3D accelerometer, a 3D gyroscope and resistive pressure sensors implemented around a CC2540 system-on-chip with an 8051 processor core, Bluetooth low energy (BLE) connectivity and flash memory buffer. The SmartStep is wirelessly interfaced to an Android smart phone application with data logging and visualization capabilities. This article focuses on low-power implementation methods and on the method developed for reliable data buffering, alleviating intermittent connectivity resulting from the user leaving the vicinity of the smart phone. The conducted tests illustrate the power consumption for several possible usage scenarios and the reliability of the data retention method. The trade-off between the power consumption and supported functionality is discussed, demonstrating that SmartStep can be worn for more than two days between battery recharges. The results of the mechanical reliability test on the SmartStep indicate that the pressure sensors in the SmartStep tolerated prolonged human wear. The SmartStep system collected more than 98.5% of the sensor data, in real usage scenarios, having intermittent connectivity with the smart phone.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.