Muscle Activation and Inertial Motion Data for Non-Invasive Classification of Activities of Daily Living

The role of the hand is crucial for the performance of activities of daily living, thereby ensuring a full and autonomous life. Its motion is controlled by a complex musculoskeletal system of approximately 38 muscles. Therefore, measuring and interpreting the muscle activation signals that drive hand motion is of great importance in many scientific domains, such as neuroscience, rehabilitation, physiotherapy, robotics, prosthetics, and biomechanics. Electromyography (EMG) can be used to carry out the neuromuscular characterization, but it is cumbersome because of the complexity of the musculoskeletal system of the forearm and hand. This paper reviews the main studies in which EMG has been applied to characterize the muscle activity of the forearm and hand during activities of daily living, with special attention to muscle synergies, which are thought to be used by the nervous system to simplify the control of the numerous muscles by actuating them in task-relevant subgroups. The state of the art of the current results are presented, which may help to guide and foster progress in many scientific domains. Furthermore, the most important challenges and open issues are identified in order to achieve a better understanding of human hand behavior, improve rehabilitation protocols, more intuitive control of prostheses, and more realistic biomechanical models.

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Non-invasive monitoring of the activities of daily living of elderly people at home – a pilot study of the usage of domestic appliances

Journal of Telemedicine and Telecare ◽

10.1258/jtt.2008.071207 ◽

2008 ◽

Vol 14 (5) ◽

pp. 231-235 ◽

Cited By ~ 28

Author(s):

Georgina Corte Franco ◽

Floriane Gallay ◽

Marc Berenguer ◽

Christine Mourrain ◽

Pascal Couturier

Keyword(s):

Pilot Study ◽

Activities Of Daily Living ◽

Elderly People ◽

Daily Living ◽

Invasive Monitoring ◽

Non Invasive ◽

Domestic Appliances ◽

At Home

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Automated Classification of Normal Control and Early-Stage Dementia Based on Activities of Daily Living (ADL) Data Acquired from Smart Home Environment

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph182413235 ◽

2021 ◽

Vol 18 (24) ◽

pp. 13235

Author(s):

Lee-Nam Kwon ◽

Dong-Hun Yang ◽

Myung-Gwon Hwang ◽

Soo-Jin Lim ◽

Young-Kuk Kim ◽

...

Keyword(s):

Activities Of Daily Living ◽

Normal Control ◽

Daily Living ◽

Smart Home ◽

Early Stage ◽

Machine Learning Techniques ◽

Evaluation Tool ◽

Dementia Patients ◽

Diagnosis Of Dementia

With the global trend toward an aging population, the increasing number of dementia patients and elderly living alone has emerged as a serious social issue in South Korea. The assessment of activities of daily living (ADL) is essential for diagnosing dementia. However, since the assessment is based on the ADL questionnaire, it relies on subjective judgment and lacks objectivity. Seven healthy seniors and six with early-stage dementia participated in the study to obtain ADL data. The derived ADL features were generated by smart home sensors. Statistical methods and machine learning techniques were employed to develop a model for auto-classifying the normal controls and early-stage dementia patients. The proposed approach verified the developed model as an objective ADL evaluation tool for the diagnosis of dementia. A random forest algorithm was used to compare a personalized model and a non-personalized model. The comparison result verified that the accuracy (91.20%) of the personalized model was higher than that (84.54%) of the non-personalized model. This indicates that the cognitive ability-based personalization showed encouraging performance in the classification of normal control and early-stage dementia and it is expected that the findings of this study will serve as important basic data for the objective diagnosis of dementia.

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Non-Intrusive Load Monitoring and Classification of Activities of Daily Living Using Residential Smart Meter Data

IEEE Transactions on Consumer Electronics ◽

10.1109/tce.2019.2918922 ◽

2019 ◽

Vol 65 (3) ◽

pp. 339-348 ◽

Cited By ~ 19

Author(s):

Michael A. Devlin ◽

Barry P. Hayes

Keyword(s):

Activities Of Daily Living ◽

Daily Living ◽

Smart Meter ◽

Load Monitoring

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Classification of Activities of Daily living using Smart Phones

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4822.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 2984-2988

Keyword(s):

Activities Of Daily Living ◽

Daily Living ◽

Human Life ◽

Smart Phone ◽

Smart Phones ◽

Machine Learning Techniques ◽

Accelerometer Sensor ◽

Learning Techniques ◽

Direct Input

Smart phones have become an integral part of everyday human life. These phones are packed with various sensors for different purposes. Most of them are used for understanding the environment in which the user uses the phone so that the device could respond rapidly. Indirectly the phone extracts context information of the users like the activity performed using accelerometer and gyroscope sensors. This information can be used for a variety of applications like home automation, smart environment, etc to perform automatic changes to the environment without direct input from the user. This paper deals with the classification of activities of daily living like walking, jogging, sitting, standing, upstairs and downstairs using the data collected from accelerometer sensor within the smart phone. A comparative analysis has been performed on different machine learning techniques for activity classification.

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Detecting hand washing activity among activities of daily living and classification of WHO hand washing techniques using wearable devices and machine learning algorithms

Healthcare Technology Letters ◽

10.1049/htl2.12018 ◽

2021 ◽

Author(s):

Yiyuan Zhang ◽

Tianwei Xue ◽

Zhenjie Liu ◽

Wei Chen ◽

Bart Vanrumste

Keyword(s):

Machine Learning ◽

Activities Of Daily Living ◽

Daily Living ◽

Learning Algorithms ◽

Wearable Devices ◽

Machine Learning Algorithms ◽

Hand Washing

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Differences in unilateral chest press muscle activation and kinematics on a stable versus unstable surface while holding one versus two dumbbells

PeerJ ◽

10.7717/peerj.1365 ◽

2015 ◽

Vol 3 ◽

pp. e1365 ◽

Cited By ~ 5

Author(s):

Jeffrey M. Patterson ◽

Andrew D. Vigotsky ◽

Nicole E. Oppenheimer ◽

Erin H. Feser

Keyword(s):

Activities Of Daily Living ◽

Range Of Motion ◽

Daily Living ◽

Muscle Activation ◽

Bench Press ◽

Emg Amplitude ◽

Unstable Surface ◽

Shoulder Complex ◽

External Oblique ◽

Internal Oblique

Training the bench press exercise on a traditional flat bench does not induce a level of instability as seen in sport movements and activities of daily living. Twenty participants were recruited to test two forms of instability: using one dumbbell rather than two and lifting on the COR bench compared to a flat bench. Electromyography (EMG) amplitudes of the pectoralis major, middle trapezius, external oblique, and internal oblique were recorded and compared. Differences in range of motion (ROM) were evaluated by measuring an angular representation of the shoulder complex. Four separate conditions of unilateral bench press were tested while lifting on a: flat bench with one dumbbell, flat bench with two dumbbells, COR Bench with one dumbbell, and COR Bench with two dumbbells. The results imply that there are no differences in EMG amplitude or ROM between the COR bench and traditional bench. However, greater ROM was found to be utilized in the single dumbbell condition, both in the COR bench and the flat bench.

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