An Individualized Machine Learning Approach for Human Body Weight Estimation Using Smart Shoe Insoles
Rapid significant weight fluctuations can indicate severe health conditions such as edema due to congestive heart failure or severe dehydration that could require prompt intervention. Daily body weighing does not accurately represent the patient’s body weight fluctuations occurring within a day. The...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/17/7418 |
| _version_ | 1797581842883280896 |
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| author | Foram Sanghavi Obafemi Jinadu Victor Oludare Karen Panetta Landry Kezebou Susan B. Roberts |
| author_facet | Foram Sanghavi Obafemi Jinadu Victor Oludare Karen Panetta Landry Kezebou Susan B. Roberts |
| author_sort | Foram Sanghavi |
| collection | DOAJ |
| description | Rapid significant weight fluctuations can indicate severe health conditions such as edema due to congestive heart failure or severe dehydration that could require prompt intervention. Daily body weighing does not accurately represent the patient’s body weight fluctuations occurring within a day. The patient’s lack of compliance with tracking their weight measurements is also a predominant issue. Using shoe insole sensors embedded into footwear could achieve accurate real-time monitoring systems for estimating continuous body weight changes. Here, the machine learning models’ predictive capabilities for continuous real-time weight estimation using the insole data are presented. The lack of availability of public datasets to feed these models is also addressed by introducing two novel datasets. The proposed framework is designed to adapt to the patient, considering several unique factors such as shoe type, posture, foot shape, and gait pattern. The proposed framework estimates the mean absolute percentage error of 0.61% and 0.74% and the MAE of 1.009 lbs. and 1.154 lbs. for the less controlled and more controlled experimental settings, respectively. This will help researchers utilize machine learning techniques for more accurate real-time continuous weight estimation using sensor data and enable more reliable aging-in-place monitoring and telehealth. |
| first_indexed | 2024-03-10T23:13:18Z |
| format | Article |
| id | doaj.art-5a588bcd845d4720bd9e6928af32fef6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:13:18Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5a588bcd845d4720bd9e6928af32fef62023-11-19T08:49:38ZengMDPI AGSensors1424-82202023-08-012317741810.3390/s23177418An Individualized Machine Learning Approach for Human Body Weight Estimation Using Smart Shoe InsolesForam Sanghavi0Obafemi Jinadu1Victor Oludare2Karen Panetta3Landry Kezebou4Susan B. Roberts5Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USADepartment of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USADepartment of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USADepartment of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USADepartment of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USAFriedman School of Nutrition Science and Policy, Tufts University, Medford, MA 02155, USARapid significant weight fluctuations can indicate severe health conditions such as edema due to congestive heart failure or severe dehydration that could require prompt intervention. Daily body weighing does not accurately represent the patient’s body weight fluctuations occurring within a day. The patient’s lack of compliance with tracking their weight measurements is also a predominant issue. Using shoe insole sensors embedded into footwear could achieve accurate real-time monitoring systems for estimating continuous body weight changes. Here, the machine learning models’ predictive capabilities for continuous real-time weight estimation using the insole data are presented. The lack of availability of public datasets to feed these models is also addressed by introducing two novel datasets. The proposed framework is designed to adapt to the patient, considering several unique factors such as shoe type, posture, foot shape, and gait pattern. The proposed framework estimates the mean absolute percentage error of 0.61% and 0.74% and the MAE of 1.009 lbs. and 1.154 lbs. for the less controlled and more controlled experimental settings, respectively. This will help researchers utilize machine learning techniques for more accurate real-time continuous weight estimation using sensor data and enable more reliable aging-in-place monitoring and telehealth.https://www.mdpi.com/1424-8220/23/17/7418human body weight estimationsmart shoe insolesmachine learningpredictive modeling |
| spellingShingle | Foram Sanghavi Obafemi Jinadu Victor Oludare Karen Panetta Landry Kezebou Susan B. Roberts An Individualized Machine Learning Approach for Human Body Weight Estimation Using Smart Shoe Insoles Sensors human body weight estimation smart shoe insoles machine learning predictive modeling |
| title | An Individualized Machine Learning Approach for Human Body Weight Estimation Using Smart Shoe Insoles |
| title_full | An Individualized Machine Learning Approach for Human Body Weight Estimation Using Smart Shoe Insoles |
| title_fullStr | An Individualized Machine Learning Approach for Human Body Weight Estimation Using Smart Shoe Insoles |
| title_full_unstemmed | An Individualized Machine Learning Approach for Human Body Weight Estimation Using Smart Shoe Insoles |
| title_short | An Individualized Machine Learning Approach for Human Body Weight Estimation Using Smart Shoe Insoles |
| title_sort | individualized machine learning approach for human body weight estimation using smart shoe insoles |
| topic | human body weight estimation smart shoe insoles machine learning predictive modeling |
| url | https://www.mdpi.com/1424-8220/23/17/7418 |
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