Privacy-Preserving Human Action Recognition with a Many-Objective Evolutionary Algorithm

Climent-Pérez, Pau; Flórez-Revuelta, Francisco

doi:10.3390/s22030764

Cited by 6 publications

(16 citation statements)

References 26 publications

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“…The only related work found on PAAL dataset is published in 2022 [5]. So, related work section is mainly constructed with recent studies that have used single three axis accelerometer signals and analyze complex human activities.…”

Section: Related Workmentioning

confidence: 99%

“…This approach can aid elderly in lifelogging and home diary applications. By looking at automated activity logs, physicians or caregivers can check the validity of activities and afterwards detect certain dementia related diseases before they begin to manifest [5]. Likewise, users can check their activity diaries and see what activities they have done.…”

Section: Introductionmentioning

confidence: 99%

“…Likewise, users can check their activity diaries and see what activities they have done. Since there are no available treatment for dementia related diseases, an HAR system that collects data for long durations in a home setting can help doctors or caregivers to analyze progression of mental decline [5].…”

Section: Introductionmentioning

confidence: 99%

“…Due to their nature, these activities can have small motion cycles and therefore they have small number of samples. These limited sample size data can have a negative effect on prediction capability of machine learning approaches [5].…”

Section: Introductionmentioning

confidence: 99%

“…The dataset includes simple movements and complex daily living activities. Also, it has the highest number of daily living activities among accelerometer-based open access datasets so far [5]. Several time and frequency domain features are extracted from a single accelerometer sensor and fed to a hybrid machine learning algorithm called Locally Weighted Random Forest (LWRF) to predict human activities.…”

Section: Introductionmentioning

confidence: 99%

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Complex Human Activity Recognition Using a Local Weighted Approach

Asuroglu

2022

IEEE Access

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Due to the life expectancy increase, there will be a workforce shortage in elderly care sector in forthcoming years. Ambient Assisted Living (AAL) systems can cope with this issue. A subset of AAL, Human activity recognition (HAR) provides an efficient way to tackle this issue. It can help with evaluating general health and welfare of elderly by automatically tracking their activities. Lifelogging and home diary applications will reduce the load on physicians and caregivers. On the other hand, complex activities play a vital role as they have high level semantic characteristics that truly represent daily life of the user. The main objective is to track these high-level semantic motions with low-cost single sensor systems with efficient machine learning frameworks. To achieve this objective, a framework is proposed to predict complex human activities from a single sensor using a machine learning approach. Time and frequency features are extracted from PAAL ADL Accelerometry Dataset and fed to Locally Weighted Random Forest (LWRF) machine learning algorithm. This algorithm is a hybrid structure that utilizes local weighting by introducing neighboring samples on Random Forest tree building phases. Proposed approach achieved 91% accuracy for HAR and 91.3% for gender recognition, outperforming other machine learning algorithms and previous study on the same dataset. This is the first study that utilize a local weighted approach for accelerometer signal domain. For prospective application, proposed framework can be embedded in lifelogging and home diary applications in home environments to track mental status of elderlies.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Complex Human Activity Recognition Using a Local Weighted Approach

Asuroglu

2022

IEEE Access

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Recognizing Hand-Based Micro Activities Using Wrist-Worn Inertial Sensors: A Zero-Shot Learning Approach

Al Machot,

Ullah,

Demrozi

2024

Lecture Notes in Computer Science

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A deep learning approach for parkinson’s disease severity assessment

Asuroglu

Ogul

2022

Health Technol.

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Purpose Parkinson’s Disease comes on top among neurodegenerative diseases affecting 10 million worldwide. To detect Parkinson’s Disease in a prior state, gait analysis is an effective choice. However, monitoring of Parkinson’s Disease using gait analysis is time consuming and exhaustive for patients and physicians. To assess severity of symptoms, a rating scale called Unified Parkinson's Disease Rating Scale is used. It determines mild and severe cases. Today, Parkinson’s Disease severity assessment is made in gait laboratories and by manual examination. These are time consuming and it is costly for health institutions to build and maintain laboratories. By using low-cost wearables and an effective model, aforementioned problems can be solved. Methods We provide a computerized solution for quantifiable assessment of Parkinson’s Disease symptoms severity. By using wearable sensors, our framework can predict exact symptom values to assess Parkinson’s Disease severity. We propose a deep learning approach that utilizes Ground Reaction Force sensors. From sensor signals, features are extracted and fed to a hybrid deep learning model. This model is the combination of Convolutional Neural Networks and Locally Weighted Random Forest. Results Proposed framework achieved 0.897, 3.009, 4.556 in terms of Correlation Coefficient, Mean Absolute Error and Root Mean Square Error, respectively. Proposed framework outperformed other machine and deep learning models. We also evaluated classification performance for disease detection. We outperformed most of the previous studies, achieving 99.5% accuracy, 98.7% sensitivity and 99.1% specificity. Conclusion This is the first study to use a deep learning regression approach to predict exact symptom value of Parkinson’s Disease patients. Results show that this approach can be effectively employed as a disease severity assessment tool using wearable sensors.

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Privacy-Preserving Human Action Recognition with a Many-Objective Evolutionary Algorithm

Cited by 6 publications

References 26 publications

Complex Human Activity Recognition Using a Local Weighted Approach

Complex Human Activity Recognition Using a Local Weighted Approach

Recognizing Hand-Based Micro Activities Using Wrist-Worn Inertial Sensors: A Zero-Shot Learning Approach

A deep learning approach for parkinson’s disease severity assessment

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