scholarly journals Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 180 ◽  
Author(s):  
José M. Ferreira ◽  
Ivan Miguel Pires ◽  
Gonçalo Marques ◽  
Nuno M. García ◽  
Eftim Zdravevski ◽  
...  
Keyword(s):  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Daily Living ◽  
Living Room ◽  
Complex Data ◽  
Machine Learning Method ◽  
Nearest Neighbour ◽  
Processing Data ◽  
Network Methods ◽  
Environment Recognition

The recognition of Activities of Daily Living (ADL) using the sensors available in off-the-shelf mobile devices with high accuracy is significant for the development of their framework. Previously, a framework that comprehends data acquisition, data processing, data cleaning, feature extraction, data fusion, and data classification was proposed. However, the results may be improved with the implementation of other methods. Similar to the initial proposal of the framework, this paper proposes the recognition of eight ADL, e.g., walking, running, standing, going upstairs, going downstairs, driving, sleeping, and watching television, and nine environments, e.g., bar, hall, kitchen, library, street, bedroom, living room, gym, and classroom, but using the Instance Based k-nearest neighbour (IBk) and AdaBoost methods as well. The primary purpose of this paper is to find the best machine learning method for ADL and environment recognition. The results obtained show that IBk and AdaBoost reported better results, with complex data than the deep neural network methods.

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 Multi-Sensor Mobile Platform for the Recognition of Activities of Daily Living and their Environments based on Artificial Neural Networks

2018 ◽  
Author(s):  
Ivan Miguel Pires ◽  
Nuno Pombo ◽  
Nuno M. Garcia ◽  
Francisco Flórez-Revuelta
Keyword(s):  
Neural Networks ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Global Positioning System ◽  
Mobile Application ◽  
Daily Living ◽  
Deep Neural Networks ◽  
Gps Receiver ◽  
Processing Data ◽  
Global Positioning

The recognition of Activities of Daily Living (ADL) and their environments based on sensors available in off-the-shelf mobile devices is an emerging topic. These devices are capable to acquire and process the sensors' data for the correct recognition of the ADL and their environments, providing a fast and reliable feedback to the user. However, the methods implemented in a mobile application for this purpose should be adapted to the low resources of these devices. This paper focuses on the demonstration of a mobile application that implements a framework, that forks their implementation in several modules, including data acquisition, data processing, data fusion and classification methods based on the sensors? data acquired from the accelerometer, gyroscope, magnetometer, microphone and Global Positioning System (GPS) receiver. The framework presented is a function of the number of sensors available in the mobile devices and implements the classification with Deep Neural Networks (DNN) that reports an accuracy between 58.02% and 89.15%.

scholarly journals Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1499 ◽  
Author(s):  
Ivan Miguel Pires ◽  
Gonçalo Marques ◽  
Nuno M. Garcia ◽  
Nuno Pombo ◽  
Francisco Flórez-Revuelta ◽  
...  
Keyword(s):  
Neural Networks ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Daily Living ◽  
Feedforward Neural Networks ◽  
Magnetic Sensors ◽  
Acoustic Sensors ◽  
Environment Recognition ◽  
The One ◽  
Classification Procedures

The identification of Activities of Daily Living (ADL) is intrinsic with the user’s environment recognition. This detection can be executed through standard sensors present in every-day mobile devices. On the one hand, the main proposal is to recognize users’ environment and standing activities. On the other hand, these features are included in a framework for the ADL and environment identification. Therefore, this paper is divided into two parts—firstly, acoustic sensors are used for the collection of data towards the recognition of the environment and, secondly, the information of the environment recognized is fused with the information gathered by motion and magnetic sensors. The environment and ADL recognition are performed by pattern recognition techniques that aim for the development of a system, including data collection, processing, fusion and classification procedures. These classification techniques include distinctive types of Artificial Neural Networks (ANN), analyzing various implementations of ANN and choosing the most suitable for further inclusion in the following different stages of the developed system. The results present 85.89% accuracy using Deep Neural Networks (DNN) with normalized data for the ADL recognition and 86.50% accuracy using Feedforward Neural Networks (FNN) with non-normalized data for environment recognition. Furthermore, the tests conducted present 100% accuracy for standing activities recognition using DNN with normalized data, which is the most suited for the intended purpose.

scholarly journals Android Library for Recognition of Activities of Daily Living: Implementation Considerations, Challenges, and Solutions

2018 ◽  
Vol 11 (1) ◽  
pp. 61-88 ◽  
Author(s):  
Ivan Miguel Pires ◽  
Maria Canavarro Teixeira ◽  
Nuno Pombo ◽  
Nuno M. Garcia ◽  
Francisco Flórez-Revuelta ◽  
...  
Keyword(s):  
Data Processing ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Assisted Living ◽  
Daily Living ◽  
Ambient Assisted Living ◽  
Living Room ◽  
Fusion Data ◽  
Student’S T ◽  
Cleaning Methods

Background:Off-the-shelf-mobile devices have several sensors available onboard that may be used for the recognition of Activities of Daily Living (ADL) and the environments where they are performed. This research is focused on the development of Ambient Assisted Living (AAL) systems, using mobile devices for the acquisition of the different types of data related to the physical and physiological conditions of the subjects and the environments. Mobile devices with the Android Operating Systems are the least expensive and exhibit the biggest market while providing a variety of models and onboard sensors.Objective:This paper describes the implementation considerations, challenges and solutions about a framework for the recognition of ADL and the environments, provided as an Android library. The framework is a function of the number of sensors available in different mobile devices and utilizes a variety of activity recognition algorithms to provide a rapid feedback to the user.Methods:The Android library includes data fusion, data processing, features engineering and classification methods. The sensors that may be used are the accelerometer, the gyroscope, the magnetometer, the Global Positioning System (GPS) receiver and the microphone. The data processing includes the application of data cleaning methods and the extraction of features, which are used with Deep Neural Networks (DNN) for the classification of ADL and environment. Throughout this work, the limitations of the mobile devices were explored and their effects have been minimized.Results:The implementation of the Android library reported an overall accuracy between 58.02% and 89.15%, depending on the number of sensors used and the number of ADL and environments recognized. Compared with the results available in the literature, the performance of the library reported a mean improvement of 2.93%, and they do not differ at the maximum found in prior work, that based on the Student’s t-test.Conclusion:This study proves that ADL like walking, going upstairs and downstairs, running, watching TV, driving, sleeping and standing activities, and the bedroom, cooking/kitchen, gym, classroom, hall, living room, bar, library and street environments may be recognized with the sensors available in off-the-shelf mobile devices. Finally, these results may act as a preliminary research for the development of a personal digital life coach with a multi-sensor mobile device commonly used daily.

scholarly journals Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living

2017 ◽  
Author(s):  
Ivan Miguel Serrano Pires ◽  
Nuno M. Garcia ◽  
Nuno Pombo ◽  
Francisco Flórez-Revuelta ◽  
Susanna Spinsante
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Data Fusion ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Daily Living ◽  
Magnetic Sensors ◽  
Pattern Recognition Techniques ◽  
Processing Data ◽  
Artificial Intelligence Methods

Several types of sensors have been available in off-the-shelf mobile devices, including motion, magnetic, vision, acoustic, and location sensors. This paper focuses on the fusion of the data acquired from motion and magnetic sensors, i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition of Activities of Daily Living (ADL) using pattern recognition techniques. The system developed in this study includes data acquisition, data processing, data fusion, and artificial intelligence methods. Artificial Neural Networks (ANN) are included in artificial intelligence methods, which are used in this study for the recognition of ADL. The purpose of this study is the creation of a new method using ANN for the identification of ADL, comparing three types of ANN, in order to achieve results with a reliable accuracy. The best accuracy was obtained with Deep Learning, which, after the application of the L2 regularization and normalization techniques on the sensors’ data, reports an accuracy of 89.51%.

scholarly journals Activities of daily living with bionic arm improved by combination training and latching filter in prosthesis control comparison

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Michael D. Paskett ◽  
Mark R. Brinton ◽  
Taylor C. Hansen ◽  
Jacob A. George ◽  
Tyler S. Davis ◽  
...  
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Activities Of Daily Living ◽  
Convolutional Neural Network ◽  
Daily Living ◽  
Complex Data ◽  
Multi Layer Perceptron ◽  
Experimental Conditions ◽  
Modified Kalman Filter ◽  
Prosthesis Control

Abstract Background Advanced prostheses can restore function and improve quality of life for individuals with amputations. Unfortunately, most commercial control strategies do not fully utilize the rich control information from residual nerves and musculature. Continuous decoders can provide more intuitive prosthesis control using multi-channel neural or electromyographic recordings. Three components influence continuous decoder performance: the data used to train the algorithm, the algorithm, and smoothing filters on the algorithm’s output. Individual groups often focus on a single decoder, so very few studies compare different decoders using otherwise similar experimental conditions. Methods We completed a two-phase, head-to-head comparison of 12 continuous decoders using activities of daily living. In phase one, we compared two training types and a smoothing filter with three algorithms (modified Kalman filter, multi-layer perceptron, and convolutional neural network) in a clothespin relocation task. We compared training types that included only individual digit and wrist movements vs. combination movements (e.g., simultaneous grasp and wrist flexion). We also compared raw vs. nonlinearly smoothed algorithm outputs. In phase two, we compared the three algorithms in fragile egg, zipping, pouring, and folding tasks using the combination training and smoothing found beneficial in phase one. In both phases, we collected objective, performance-based (e.g., success rate), and subjective, user-focused (e.g., preference) measures. Results Phase one showed that combination training improved prosthesis control accuracy and speed, and that the nonlinear smoothing improved accuracy but generally reduced speed. Phase one importantly showed simultaneous movements were used in the task, and that the modified Kalman filter and multi-layer perceptron predicted more simultaneous movements than the convolutional neural network. In phase two, user-focused metrics favored the convolutional neural network and modified Kalman filter, whereas performance-based metrics were generally similar among all algorithms. Conclusions These results confirm that state-of-the-art algorithms, whether linear or nonlinear in nature, functionally benefit from training on more complex data and from output smoothing. These studies will be used to select a decoder for a long-term take-home trial with implanted neuromyoelectric devices. Overall, clinical considerations may favor the mKF as it is similar in performance, faster to train, and computationally less expensive than neural networks.

scholarly journals A Modular Mobile Robotic Platform to Assist People with Different Degrees of Disability

2021 ◽  
Vol 11 (15) ◽  
pp. 7130
Author(s):  
Jose M. Catalan ◽  
Andrea Blanco ◽  
Arturo Bertomeu-Motos ◽  
Jose V. Garcia-Perez ◽  
Miguel Almonacid ◽  
...  
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Occupational Therapists ◽  
Living Room ◽  
Key Factors ◽  
Robotic Platform ◽  
Robotic Exoskeleton ◽  
Simulated Home Environment ◽  
Wheel Chair ◽  
The Subject

Robotics to support elderly people in living independently and to assist disabled people in carrying out the activities of daily living independently have demonstrated good results. Basically, there are two approaches: one of them is based on mobile robot assistants, such as Care-O-bot, PR2, and Tiago, among others; the other one is the use of an external robotic arm or a robotic exoskeleton fixed or mounted on a wheelchair. In this paper, a modular mobile robotic platform to assist moderately and severely impaired people based on an upper limb robotic exoskeleton mounted on a robotized wheel chair is presented. This mobile robotic platform can be customized for each user’s needs by exploiting its modularity. Finally, experimental results in a simulated home environment with a living room and a kitchen area, in order to simulate the interaction of the user with different elements of a home, are presented. In this experiment, a subject suffering from multiple sclerosis performed different activities of daily living (ADLs) using the platform in front of a group of clinicians composed of nurses, doctors, and occupational therapists. After that, the subject and the clinicians replied to a usability questionnaire. The results were quite good, but two key factors arose that need to be improved: the complexity and the cumbersome aspect of the platform.

scholarly journals From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

Sensors ◽  
2016 ◽  
Vol 16 (2) ◽  
pp. 184 ◽  
Author(s):  
Ivan Pires ◽  
Nuno Garcia ◽  
Nuno Pombo ◽  
Francisco Flórez-Revuelta
Keyword(s):  
Data Fusion ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Daily Living ◽  
State Of The Art ◽  
The State ◽  
Sensor Data ◽  
Battery Life ◽  
Sensor Data Fusion ◽  
Processing Power

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs).

scholarly journals Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 509 ◽  
Author(s):  
Ivan Miguel Pires ◽  
Gonçalo Marques ◽  
Nuno M. Garcia ◽  
Francisco Flórez-Revuelta ◽  
Maria Canavarro Teixeira ◽  
...  
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Mobile Device ◽  
Daily Living ◽  
Accelerometer Data ◽  
Pattern Recognition Techniques ◽  
Multiple Sensors ◽  
Computational Resources

The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs’ identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).

scholarly journals Activities of Daily Living with Bionic Arm Improved by Combination Training and Latching Filter in Prosthesis Control Comparison

2020 ◽  
Author(s):  
Michael D. Paskett ◽  
Mark R. Brinton ◽  
Taylor C. Hansen ◽  
Jacob A. George ◽  
Tyler S. Davis ◽  
...  
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Activities Of Daily Living ◽  
Convolutional Neural Network ◽  
Daily Living ◽  
Complex Data ◽  
Multi Layer Perceptron ◽  
Experimental Conditions ◽  
Modified Kalman Filter ◽  
Prosthesis Control

AbstractBackgroundAdvanced prostheses can restore function and improve quality of life for individuals with amputations. Unfortunately, most commercial control strategies do not utilize the rich control information from residual nerves and musculature. Continuous decoders can provide more intuitive prosthesis control from multi-channel neural or electromyographic recordings. Three components influence continuous decoder performance: the data used to train the algorithm, the algorithm, and smoothing filters on the algorithm’s output. As individual groups often focus on a single decoder, very few studies compare different decoders using otherwise similar experimental conditions.MethodsWe completed a two-phase head-to-head comparison of 12 continuous decoders using activities of daily living. In phase one, we compared two training types and a smoothing filter with three algorithms (modified Kalman filter, multi-layer perceptron, and convolutional neural network) in a clothespin relocation task. We compared training types that included data with only individual digit and wrist movements vs. combination movements (e.g., simultaneous grasp and wrist flexion). We also compared raw vs. nonlinearly smoothed algorithm outputs. In phase two, we compared the three algorithms in fragile egg, zipping, pouring, and folding tasks using the combination training and smoothing found beneficial in phase one. In both phases, we collected objective, performance-based (e.g., success rate) and subjective, user-focused (e.g., preference) measures.ResultsPhase one showed that combination training improved prosthesis control accuracy and speed, and that the nonlinear smoothing improved accuracy but generally reduced speed. Phase one importantly showed simultaneous movements were used in the task, and that the modified Kalman filter and multi-layer perceptron predicted more simultaneous movements than the convolutional neural network. In phase two, user-focused metrics favored the convolutional neural network and modified Kalman filter, whereas performance-based metrics were generally similar among all algorithms.ConclusionsThese results confirm that state-of-the-art algorithms, whether linear or nonlinear in nature, functionally benefit from training on more complex data and from output smoothing. These studies will be used to select a decoder for a long-term take-home trial with implanted neuromyoelectric devices. Overall, clinical considerations may favor the mKF as it is similar in performance, faster to train, and computationally less expensive than neural networks.

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