scholarly journals Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning

2021 ◽  
Vol 191 ◽  
pp. 361-366
Author(s):  
García-Restrepo Johanna ◽  
Ariza-Colpas Paola Patricia ◽  
Oñate-Bowen Alvaro Agustín ◽  
Suarez-Brieva Eydy del Carmen ◽  
Urina-Triana Miguel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Activities Of Daily Living ◽  
Predictive Model ◽  
Daily Living ◽  
Indoor Environments ◽  
Classification Techniques

scholarly journals Streamlining the KOOS Activities of Daily Living Subscale Using Machine Learning

2020 ◽  
Vol 8 (3) ◽  
pp. 232596712091044 ◽  
Author(s):  
Ashim Gupta ◽  
Ajish S.R. Potty ◽  
Deepak Ganta ◽  
R. Justin Mistovich ◽  
Sreeram Penna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Activities Of Daily Living ◽  
Functional Outcome ◽  
Acl Reconstruction ◽  
Daily Living ◽  
High Accuracy ◽  
Quality Data ◽  
Specific Patient ◽  
Patient Reported ◽  
Essential Questions

Background: Functional outcome scores provide valuable data, yet they can be burdensome to patients and require significant resources to administer. The Knee injury and Osteoarthritis Outcome Score (KOOS) is a knee-specific patient-reported outcome measure (PROM) and is validated for anterior cruciate ligament (ACL) reconstruction outcomes. The KOOS requires 42 questions in 5 subscales. We utilized a machine learning (ML) algorithm to determine whether the number of questions and the resultant burden to complete the survey can be lowered in a subset (activities of daily living; ADL) of KOOS, yet still provide identical data. Hypothesis: Fewer questions than the 17 currently provided are actually needed to predict KOOS ADL subscale scores with high accuracy. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: Pre- and postoperative patient-reported KOOS ADL scores were obtained from the Surgical Outcome System (SOS) data registry for patients who had ACL reconstruction. Categorical Boosting (CatBoost) ML models were built to analyze each question and its value in predicting the patient’s actual functional outcome (ie, KOOS ADL score). A streamlined set of minimal essential questions were then identified. Results: The SOS registry contained 6185 patients who underwent ACL reconstruction. A total of 2525 patients between the age of 16 and 50 years had completed KOOS ADL scores presurgically and 3 months postoperatively. The data set consisted of 51.84% male patients and 48.16% female patients, with a mean age of 29 years. The CatBoost model predicted KOOS ADL scores with high accuracy when only 6 questions were asked ( R2 = 0.95), similar to when all 17 questions of the subscale were asked ( R2 = 0.99). Conclusion: ML algorithms successfully identified the essential questions in the KOOS ADL questionnaire. Only 35% (6/17) of KOOS ADL questions (descending stairs, ascending stairs, standing, walking on flat surface, putting on socks/stockings, and getting on/off toilet) are needed to predict KOOS ADL scores with high accuracy after ACL reconstruction. ML can be utilized successfully to streamline the burden of patient data collection. This, in turn, can potentially lead to improved patient reporting, increased compliance, and increased utilization of PROMs while still providing quality data.

scholarly journals Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models

2018 ◽  
Vol 124 (2) ◽  
pp. 473-481 ◽  
Author(s):  
Thomas Beltrame ◽  
Robert Amelard ◽  
Alexander Wong ◽  
Richard L. Hughson
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Health Status ◽  
Early Detection ◽  
System Dynamics ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Wearable Sensors ◽  
Frequency Domain Analysis ◽  
Aerobic System

Physical activity levels are related through algorithms to the energetic demand, with no information regarding the integrity of the multiple physiological systems involved in the energetic supply. Longitudinal analysis of the oxygen uptake (V̇o2) by wearable sensors in realistic settings might permit development of a practical tool for the study of the longitudinal aerobic system dynamics (i.e., V̇o2 kinetics). This study evaluated aerobic system dynamics based on predicted V̇o2 data obtained from wearable sensors during unsupervised activities of daily living (μADL). Thirteen healthy men performed a laboratory-controlled moderate exercise protocol and were monitored for ≈6 h/day for 4 days (μADL data). Variables derived from hip accelerometer (ACCHIP), heart rate monitor, and respiratory bands during μADL were extracted and processed by a validated random forest regression model to predict V̇o2. The aerobic system analysis was based on the frequency-domain analysis of ACCHIP and predicted V̇o2 data obtained during μADL. Optimal samples for frequency domain analysis (constrained to ≤0.01 Hz) were selected when ACCHIP was higher than 0.05 g at a given frequency (i.e., participants were active). The temporal characteristics of predicted V̇o2 data during μADL correlated with the temporal characteristics of measured V̇o2 data during laboratory-controlled protocol ([Formula: see text] = 0.82, P < 0.001, n = 13). In conclusion, aerobic system dynamics can be investigated during unsupervised activities of daily living by wearable sensors. Although speculative, these algorithms have the potential to be incorporated into wearable systems for early detection of changes in health status in realistic environments by detecting changes in aerobic response dynamics. NEW & NOTEWORTHY The early detection of subclinical aerobic system impairments might be indicative of impaired physiological reserves that impact the capacity for physical activity. This study is the first to use wearable sensors in unsupervised activities of daily living in combination with novel machine learning algorithms to investigate the aerobic system dynamics with the potential to contribute to models of functional health status and guide future individualized health care in the normal population.

scholarly journals Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Mobile Devices’ Sensors

2018 ◽  
Author(s):  
Ivan Miguel Pires ◽  
Nuno M. Garcia ◽  
Nuno Pombo ◽  
Francisco Flórez-Revuelta ◽  
Susanna Spinsante
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Data Fusion ◽  
Data Processing ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Daily Living ◽  
Automatic Identification ◽  
Internet Connection ◽  
Processing Data

Sensors available on mobile devices allow the automatic identification of Activities of Daily Living (ADL). This paper describes an approach for the creation of a framework for the identification of ADL, taking in account several concepts, including data acquisition, data processing, data fusion, pattern recognition, and machine learning. These concepts can be mapped in a module of the framework, including the use and creation of several algorithms. For the development of a framework that works in several conditions, e.g., without Internet connection, these algorithms should take in account the hardware and software limitations of the mobile devices to run all main tasks locally. The main purpose of this paper is related to the presentation the sensors, algorithms, and architecture of the proposed approach.

scholarly journals Predicting activities of daily living for cancer patients using an ontology-guided machine learning methodology

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Hua Min ◽  
Hedyeh Mobahi ◽  
Katherine Irvin ◽  
Sanja Avramovic ◽  
Janusz Wojtusiak
Keyword(s):  
Machine Learning ◽  
Activities Of Daily Living ◽  
Cancer Patients ◽  
Daily Living

scholarly journals Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study

2021 ◽  
Vol 18 (4) ◽  
pp. 1634
Author(s):  
Pin-Wei Chen ◽  
Nathan A. Baune ◽  
Igor Zwir ◽  
Jiayu Wang ◽  
Victoria Swamidass ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Wearable Sensors ◽  
Classification Model ◽  
Support Vector ◽  
Stroke Patients ◽  
Extreme Gradient Boosting ◽  
Testing Data

Measuring activities of daily living (ADLs) using wearable technologies may offer higher precision and granularity than the current clinical assessments for patients after stroke. This study aimed to develop and determine the accuracy of detecting different ADLs using machine-learning (ML) algorithms and wearable sensors. Eleven post-stroke patients participated in this pilot study at an ADL Simulation Lab across two study visits. We collected blocks of repeated activity (“atomic” activity) performance data to train our ML algorithms during one visit. We evaluated our ML algorithms using independent semi-naturalistic activity data collected at a separate session. We tested Decision Tree, Random Forest, Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) for model development. XGBoost was the best classification model. We achieved 82% accuracy based on ten ADL tasks. With a model including seven tasks, accuracy improved to 90%. ADL tasks included chopping food, vacuuming, sweeping, spreading jam or butter, folding laundry, eating, brushing teeth, taking off/putting on a shirt, wiping a cupboard, and buttoning a shirt. Results provide preliminary evidence that ADL functioning can be predicted with adequate accuracy using wearable sensors and ML. The use of external validation (independent training and testing data sets) and semi-naturalistic testing data is a major strength of the study and a step closer to the long-term goal of ADL monitoring in real-world settings. Further investigation is needed to improve the ADL prediction accuracy, increase the number of tasks monitored, and test the model outside of a laboratory setting.

scholarly journals A Machine Learning-Based Aging Measure Among Middle-Aged and Older Chinese Adults: The China Health and Retirement Longitudinal Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Xingqi Cao ◽  
Guanglai Yang ◽  
Xurui Jin ◽  
Liu He ◽  
Xueqin Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Longitudinal Study ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Gradient Boosting ◽  
Biological Aging ◽  
Middle Aged ◽  
Chinese Adults ◽  
Older Chinese ◽  
Older Chinese Adults

Objective: Biological age (BA) has been accepted as a more accurate proxy of aging than chronological age (CA). This study aimed to use machine learning (ML) algorithms to estimate BA in the Chinese population.Materials and methods: We used data from 9,771 middle-aged and older Chinese adults (≥45 years) in the 2011/2012 wave of the China Health and Retirement Longitudinal Study and followed until 2018. We used several ML algorithms (e.g., Gradient Boosting Regressor, Random Forest, CatBoost Regressor, and Support Vector Machine) to develop new measures of biological aging (ML-BAs) based on physiological biomarkers. R-squared value and mean absolute error (MAE) were used to determine the optimal performance of these ML-BAs. We used logistic regression models to examine the associations of the best ML-BA and a conventional aging measure—Klemera and Doubal method-BA (KDM-BA) we previously developed—with physical disability and mortality, respectively.Results: The Gradient Boosting Regression model performed the best, resulting in an ML-BA with an R-squared value of 0.270 and an MAE of 6.519. This ML-BA was significantly associated with disability in basic activities of daily living, instrumental activities of daily living, lower extremity mobility, and upper extremity mobility, and mortality, with odds ratios ranging from 1 to 7% (per 1-year increment in ML-BA, all P &lt; 0.001), independent of CA. These associations were generally comparable to that of KDM-BA.Conclusion: This study provides a valid ML-based measure of biological aging for middle-aged and older Chinese adults. These findings support the application of ML in geroscience research and may help facilitate preventive and geroprotector intervention studies.

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