Smart Insole Monitoring System for Fall Detection and Bad Plantar Pressure

Saidani, Salma; Haddad, Rim; Bouallègue, Ridha; Shubair, Raed M.

doi:10.1007/978-3-030-99619-2_20

Lecture Notes in Networks and Systems

2022

DOI: 10.1007/978-3-030-99619-2_20

|View full text |Cite

Smart Insole Monitoring System for Fall Detection and Bad Plantar Pressure

Salma Saidani

Rim Haddad

Ridha Bouallègue

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In existing studies [4][5][6][7][8][9], off-the-shelf Force Sensitive Resistors (FSR), accelerometers, and gyroscopes are utilized to fabricate pressure insoles designed to protect user privacy and enable continuous monitoring capabilities. However, due to the size and shape constraints of FSRs, sensors can only be placed at critical positions on the sole of the foot, allowing for the detection of falls and gait but limiting the ability to capture a richer set of motion information from the insole.…”

mentioning

confidence: 99%

PIFall: A Pressure Insole-Based Fall Detection System for the Elderly Using ResNet3D

Guo,

Liu,

et al. 2024

Electronics

View full text Add to dashboard Cite

Falls among the elderly are a significant public health issue, resulting in about 684,000 deaths annually. Such incidents often lead to severe consequences including fractures, contusions, and cranial injuries, immensely affecting the quality of life and independence of the elderly. Existing fall detection methods using cameras and wearable sensors face challenges such as privacy concerns, blind spots in vision and being troublesome to wear. In this paper, we propose PIFall, a Pressure Insole-Based Fall Detection System for the Elderly, utilizing the ResNet3D algorithm. Initially, we design and fabricate a pair of insoles equipped with low-cost resistive films to measure plantar pressure, arranging 5×9 pressure sensors on each insole. Furthermore, we present a fall detection method that combines ResNet(2+1)D with an insole-based sensor matrix, utilizing time-series ‘stress videos’ derived from pressure map data as input. Lastly, we collect data on 12 different actions from five subjects, including fall risk activities specifically designed to be easily confused with actual falls. The system achieves an overall accuracy of 91% in detecting falls and 94% in identifying specific fall actions. Additionally, feedback is gathered from eight elderly individuals using a structured questionnaire to assess user experience and satisfaction with the pressure insoles.

show abstract

mentioning

confidence: 99%

PIFall: A Pressure Insole-Based Fall Detection System for the Elderly Using ResNet3D

Guo,

Liu,

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

Application of Smart Insoles for Recognition of Activities of Daily Living: A Systematic Review

D’arco,

Mccalmont,

Wang

et al. 2024

ACM Trans. Comput. Healthcare

View full text Add to dashboard Cite

Recent years have witnessed the increasing literature on using smart insoles in health and well-being, and yet, their capability of daily living activity recognition has not been reviewed. This paper addressed this need and provided a systematic review of smart insole-based systems in the recognition of Activities of Daily Living (ADLs). The review followed the PRISMA guidelines, assessing the sensing elements used, the participants involved, the activities recognised, and the algorithms employed. The findings demonstrate the feasibility of using smart insoles for recognising ADLs, showing their high performance in recognising ambulation and physical activities involving the lower body, ranging from 70% to 99.8% of Accuracy, with 13 studies over 95%. The preferred solutions have been those including machine learning. A lack of existing publicly available datasets has been identified, and the majority of the studies were conducted in controlled environments. Furthermore, no studies assessed the impact of different sampling frequencies during data collection, and a trade-off between comfort and performance has been identified between the solutions. In conclusion, real-life applications were investigated showing the benefits of smart insoles over other solutions and placing more emphasis on the capabilities of smart insoles.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Smart Insole Monitoring System for Fall Detection and Bad Plantar Pressure

Cited by 2 publications

References 22 publications

PIFall: A Pressure Insole-Based Fall Detection System for the Elderly Using ResNet3D

PIFall: A Pressure Insole-Based Fall Detection System for the Elderly Using ResNet3D

Application of Smart Insoles for Recognition of Activities of Daily Living: A Systematic Review

Contact Info

Product

Resources

About