Human Activity Recognition

Labrador, Miguel A.; Yejas, Oscar D. Lara

doi:10.1201/b16098

Cited by 33 publications

(3 citation statements)

References 7 publications

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“…These issues cannot be sorted out by generic means, thus making the system costly and complex. To overcome these hurdles and limitations, in the past few years, sensors have been used to extract movement data with minimum interference of any unwanted medium [87][88][89].…”

Section: Human Activity Recognition In Rhmsmentioning

confidence: 99%

Remote Health Monitoring Systems for Elderly People: A Survey

Ahmed

Irfan²,

Masood

et al. 2023

Sensors

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This paper addresses the growing demand for healthcare systems, particularly among the elderly population. The need for these systems arises from the desire to enable patients and seniors to live independently in their homes without relying heavily on their families or caretakers. To achieve substantial improvements in healthcare, it is essential to ensure the continuous development and availability of information technologies tailored explicitly for patients and elderly individuals. The primary objective of this study is to comprehensively review the latest remote health monitoring systems, with a specific focus on those designed for older adults. To facilitate a comprehensive understanding, we categorize these remote monitoring systems and provide an overview of their general architectures. Additionally, we emphasize the standards utilized in their development and highlight the challenges encountered throughout the developmental processes. Moreover, this paper identifies several potential areas for future research, which promise further advancements in remote health monitoring systems. Addressing these research gaps can drive progress and innovation, ultimately enhancing the quality of healthcare services available to elderly individuals. This, in turn, empowers them to lead more independent and fulfilling lives while enjoying the comforts and familiarity of their own homes. By acknowledging the importance of healthcare systems for the elderly and recognizing the role of information technologies, we can address the evolving needs of this population. Through ongoing research and development, we can continue to enhance remote health monitoring systems, ensuring they remain effective, efficient, and responsive to the unique requirements of elderly individuals.

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Section: Human Activity Recognition In Rhmsmentioning

confidence: 99%

Remote Health Monitoring Systems for Elderly People: A Survey

Ahmed

Irfan²,

Masood

et al. 2023

Sensors

View full text Add to dashboard Cite

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“…Human Activity Recognition is an introduction technology that allows a system to detect activities carried out by humans [1]. The introduction is grouped into two activities, namely simple activities and complex activities.…”

Section: Introductionmentioning

confidence: 99%

Classification of Multiclass Ensemble SVM for Human Activities based on Sensor Accelerometer and Gyroscope

Wungo

Mardewi²,

Aziz

et al. 2023

Ilk. J. Ilm.

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Human Activity Recognition is technology introduced to recognize human activities. Several technologies that have been applied are Accelerometer sensors, Gyroscope sensors, Cameras, and GPS. The selection of the Support Vector Machine algorithm is due to its capabilities to minimize errors in training data sets and the Curse of dimensionality which can estimate parameters as well as its ability to find the best hyperplane that separates two classes. The SVM algorithm was originally developed for the classification of two classes. Problem raised if there are more than two classes. In addition, the performance will not optimal for the large-scale data. Therefore, modification the current design is needed. An ensemble technique can be used to combine the Support Vector Machine algorithm with the bagging algorithm. This study proposes the application of an ensemble SVM algorithm to classify human activities based on accelerometers and gyroscope sensors on smartphones. The total data is 13725 records with 4575 representatives of each class. From the results of the overall data partition carried out in the calcification process using the ensemble SVM algorithm, the best performance was generated when comparing datasets with 80% training data and 20% test data from a total of 13725 records because it succeeded in increasing accuracy, precision, and sensitivity.

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“…An example is the field of activity recognition, which tries to classify physical activities, such as walking or ascending stairs, currently performed by a person, based on sensor data. An extensive overview and introduction of the field of activity recognition using inertial sensors of either wearables or smartphones is provided in [ 1 , 2 ]. In earlier works, this was mostly done using multiple wearable sensors attached to several positions on the human body [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Typical Locomotion Activities Based on the Sensor Data of a Smartphone in Pocket or Hand

Ebner

Fetzer

Bullmann

et al. 2020

Sensors

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With the ubiquity of smartphones, the interest in indoor localization as a research area grew. Methods based on radio data are predominant, but due to the susceptibility of these radio signals to a number of dynamic influences, good localization solutions usually rely on additional sources of information, which provide relative information about the current location. Part of this role is often taken by the field of activity recognition, e.g., by estimating whether a pedestrian is currently taking the stairs. This work presents different approaches for activity recognition, considering the four most basic locomotion activities used when moving around inside buildings: standing, walking, ascending stairs, and descending stairs, as well as an additional messing around class for rejections. As main contribution, we introduce a novel approach based on analytical transformations combined with artificially constructed sensor channels, and compare that to two approaches adapted from existing literature, one based on codebooks, the other using statistical features. Data is acquired using accelerometer and gyroscope only. In addition to the most widely adopted use-case of carrying the smartphone in the trouser pockets, we will equally consider the novel use-case of hand-carried smartphones. This is required as in an indoor localization scenario, the smartphone is often used to display a user interface of some navigation application and thus needs to be carried in hand. For evaluation the well known MobiAct dataset for the pocket-case as well as a novel dataset for the hand-case were used. The approach based on analytical transformations surpassed the other approaches resulting in accuracies of 98.0% for pocket-case and 81.8% for the hand-case trained on the combination of both datasets. With activity recognition in the supporting role of indoor localization, this accuracy is acceptable, but has room for further improvement.

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Human Activity Recognition

Cited by 33 publications

References 7 publications

Remote Health Monitoring Systems for Elderly People: A Survey

Remote Health Monitoring Systems for Elderly People: A Survey

Classification of Multiclass Ensemble SVM for Human Activities based on Sensor Accelerometer and Gyroscope

Recognition of Typical Locomotion Activities Based on the Sensor Data of a Smartphone in Pocket or Hand

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