Rank Pooling Approach for Wearable Sensor-Based ADLs Recognition

Nisar, Muhammad Adeel; Shirahama, Kimiaki; Li, Frédéric; Huang, Xinyu; Grzegorzek, Marcin

doi:10.3390/s20123463

Cited by 17 publications

(34 citation statements)

References 40 publications

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“…Beyond the use of EHR data, the constructed CBIT could be enhanced by sensor data allowing for continuous patient monitoring and be integrated with the presented approach. Such data can aid assessment, particularly for ADLs that measure patient movement [46][47][48].…”

Section: Resultsmentioning

confidence: 99%

Computational Barthel Index: an automated tool for assessing and predicting activities of daily living among nursing home patients

Wojtusiak

Asadzadehzanjani

Levy³

et al. 2021

BMC Med Inform Decis Mak

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Background Assessment of functional ability, including activities of daily living (ADLs), is a manual process completed by skilled health professionals. In the presented research, an automated decision support tool, the Computational Barthel Index Tool (CBIT), was constructed that can automatically assess and predict probabilities of current and future ADLs based on patients’ medical history. Methods The data used to construct the tool include the demographic information, inpatient and outpatient diagnosis codes, and reported disabilities of 181,213 residents of the Department of Veterans Affairs’ (VA) Community Living Centers. Supervised machine learning methods were applied to construct the CBIT. Temporal information about times from the first and the most recent occurrence of diagnoses was encoded. Ten-fold cross-validation was used to tune hyperparameters, and independent test sets were used to evaluate models using AUC, accuracy, recall and precision. Random forest achieved the best model quality. Models were calibrated using isotonic regression. Results The unabridged version of CBIT uses 578 patient characteristics and achieved average AUC of 0.94 (0.93–0.95), accuracy of 0.90 (0.89–0.91), precision of 0.91 (0.89–0.92), and recall of 0.90 (0.84–0.95) when re-evaluating patients. CBIT is also capable of predicting ADLs up to one year ahead, with accuracy decreasing over time, giving average AUC of 0.77 (0.73–0.79), accuracy of 0.73 (0.69–0.80), precision of 0.74 (0.66–0.81), and recall of 0.69 (0.34–0.96). A simplified version of CBIT with 50 top patient characteristics reached performance that does not significantly differ from full CBIT. Conclusion Discharge planners, disability application reviewers and clinicians evaluating comparative effectiveness of treatments can use CBIT to assess and predict information on functional status of patients.

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Section: Resultsmentioning

confidence: 99%

Computational Barthel Index: an automated tool for assessing and predicting activities of daily living among nursing home patients

Wojtusiak

Asadzadehzanjani

Levy³

et al. 2021

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

Section: Resultsmentioning

confidence: 99%

Computational Barthel Index: An Automated Tool for Assessing and Predicting Activities of Daily Living Among Nursing Home Patients

Wojtusiak

Asadzadehzanjani

Levy

et al. 2020

Preprint

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Background: Assessment of functional ability, including Activities of Daily Living (ADLs), is a manual process completed by skilled health professionals. In the presented research, an automated decision support tool, the Computational Barthel Index Tool (CBIT), was constructed that can automatically assess and predict probabilities of current and future ADLs based on patients’ medical history.Methods: The data used to construct the tool include the demographic information, inpatient and outpatient diagnosis codes, and reported disabilities of 181,213 residents of the Department of Veterans Affairs’ (VA) Community Living Centers. Supervised machine learning methods were applied to construct the CBIT. Temporal information about times from the first and the most recent occurrence of diagnoses was encoded. Ten-fold cross-validation was used to tune hyperparameters, and independent test sets were used to evaluate models using AUC, accuracy, recall and precision. Random forest achieved the best model quality. Models were calibrated using isotonic regression.Results: The unabridged version of CBIT uses 578 patient characteristics and achieved average AUC of 0.94 (0.93-0.95), accuracy of 0.90 (0.89-0.91), precision of 0.91 (0.89-0.92), and recall of 0.90 (0.84-0.95) when re-evaluating patients. CBIT is also capable of predicting ADLs up to one year ahead, with accuracy decreasing over time, giving average AUC of 0.77 (0.73-0.79), accuracy of 0.73 (0.69-0.80), precision of 0.74 (0.66-0.81), and recall of 0.69 (0.34-0.96). A simplified version of CBIT with 50 top patient characteristics reached performance that does not significantly differ from full CBIT.Conclusion: Discharge planners, disability application reviewers and clinicians evaluating comparative effectiveness of treatments can use CBIT to assess and predict information on functional status of patients.

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“…Typically these activities are recorded using multiple electronic devices. In this paper, we used the CogAge dataset [15] which is collected by using three unobtrusive wearable devices: smartphone, smartwatch, and smart glasses. In particular, the LG G5 smartphone was placed in the proband's front left pocket of the jeans.…”

Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

“…Second, several experiments are performed to analyze the impact of different hyper-parameters during the computation of feature descriptors, the selection of optimal features that represent the composite activities, and the evaluation of different classification algorithms. We used the CogAge dataset [15] to evaluate the performance of our proposed algorithm which contains the data of 7 composite activities performed by 6 different subjects in different time intervals using three wearable devices: smartphone, smartwatch, and smart glasses. Each of the composite activities can be represented using the combination of 61 atomic activities.…”

Section: Data Acquisitionmentioning

confidence: 99%

A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data

Amjad

Khan

Nisar

et al. 2021

Sensors

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Human activity recognition (HAR) aims to recognize the actions of the human body through a series of observations and environmental conditions. The analysis of human activities has drawn the attention of the research community in the last two decades due to its widespread applications, diverse nature of activities, and recording infrastructure. Lately, one of the most challenging applications in this framework is to recognize the human body actions using unobtrusive wearable motion sensors. Since the human activities of daily life (e.g., cooking, eating) comprises several repetitive and circumstantial short sequences of actions (e.g., moving arm), it is quite difficult to directly use the sensory data for recognition because the multiple sequences of the same activity data may have large diversity. However, a similarity can be observed in the temporal occurrence of the atomic actions. Therefore, this paper presents a two-level hierarchical method to recognize human activities using a set of wearable sensors. In the first step, the atomic activities are detected from the original sensory data, and their recognition scores are obtained. Secondly, the composite activities are recognized using the scores of atomic actions. We propose two different methods of feature extraction from atomic scores to recognize the composite activities, and they include handcrafted features and the features obtained using the subspace pooling technique. The proposed method is evaluated on the large publicly available CogAge dataset, which contains the instances of both atomic and composite activities. The data is recorded using three unobtrusive wearable devices: smartphone, smartwatch, and smart glasses. We also investigated the performance evaluation of different classification algorithms to recognize the composite activities. The proposed method achieved 79% and 62.8% average recognition accuracies using the handcrafted features and the features obtained using subspace pooling technique, respectively. The recognition results of the proposed technique and their comparison with the existing state-of-the-art techniques confirm its effectiveness.

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Rank Pooling Approach for Wearable Sensor-Based ADLs Recognition

Cited by 17 publications

References 40 publications

Computational Barthel Index: an automated tool for assessing and predicting activities of daily living among nursing home patients

Computational Barthel Index: an automated tool for assessing and predicting activities of daily living among nursing home patients

Computational Barthel Index: An Automated Tool for Assessing and Predicting Activities of Daily Living Among Nursing Home Patients

A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data

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