Toward Smart Footwear to Track Frailty Phenotypes—Using Propulsion Performance to Determine Frailty
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 con...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-06-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/6/1763 |
| _version_ | 1811306350967259136 |
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| author | Hadi Rahemi Hung Nguyen Hyoki Lee Bijan Najafi |
| author_facet | Hadi Rahemi Hung Nguyen Hyoki Lee Bijan Najafi |
| author_sort | Hadi Rahemi |
| collection | DOAJ |
| description | 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 83.2–95.8%. 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. |
| first_indexed | 2024-04-13T08:42:53Z |
| format | Article |
| id | doaj.art-0490afa6f3e64cdaace17c4017030248 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:42:53Z |
| publishDate | 2018-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0490afa6f3e64cdaace17c40170302482022-12-22T02:53:48ZengMDPI AGSensors1424-82202018-06-01186176310.3390/s18061763s18061763Toward Smart Footwear to Track Frailty Phenotypes—Using Propulsion Performance to Determine FrailtyHadi Rahemi0Hung Nguyen1Hyoki Lee2Bijan Najafi3Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USAInterdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USAInterdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USAInterdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USAFrailty 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 83.2–95.8%. 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.http://www.mdpi.com/1424-8220/18/6/1763frailtygaitartificial neural networkpropulsionagingwearable sensorwalkingsmart footwear |
| spellingShingle | Hadi Rahemi Hung Nguyen Hyoki Lee Bijan Najafi Toward Smart Footwear to Track Frailty Phenotypes—Using Propulsion Performance to Determine Frailty Sensors frailty gait artificial neural network propulsion aging wearable sensor walking smart footwear |
| title | Toward Smart Footwear to Track Frailty Phenotypes—Using Propulsion Performance to Determine Frailty |
| title_full | Toward Smart Footwear to Track Frailty Phenotypes—Using Propulsion Performance to Determine Frailty |
| title_fullStr | Toward Smart Footwear to Track Frailty Phenotypes—Using Propulsion Performance to Determine Frailty |
| title_full_unstemmed | Toward Smart Footwear to Track Frailty Phenotypes—Using Propulsion Performance to Determine Frailty |
| title_short | Toward Smart Footwear to Track Frailty Phenotypes—Using Propulsion Performance to Determine Frailty |
| title_sort | toward smart footwear to track frailty phenotypes using propulsion performance to determine frailty |
| topic | frailty gait artificial neural network propulsion aging wearable sensor walking smart footwear |
| url | http://www.mdpi.com/1424-8220/18/6/1763 |
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