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21 February 2018

Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Sensors in Mobile Devices

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and
1
Instituto de Telecomunicações, Universidade da Beira Interior, 6201-001 Covilhã, Portugal
2
Altranportugal, 1990-096 Lisbon, Portugal
3
ALLab—Assisted Living Computing and Telecommunications Laboratory, Computing Science Department, Universidade da Beira Interior, 6201-001 Covilhã, Portugal
4
ECATI, Universidade Lusófona de Humanidades e Tecnologias, 1749-024 Lisbon, Portugal
Sensors2018, 18(2), 640;https://doi.org/10.3390/s18020640
This article belongs to the Special Issue Smart Sensors for Mechatronic and Robotic Systems
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Abstract

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 into account several concepts, including data acquisition, data processing, data fusion, and pattern recognition. These concepts can be mapped onto different modules of the framework. The proposed framework should perform the identification of ADL without Internet connection, performing these tasks locally on the mobile device, taking in account the hardware and software limitations of these devices. The main purpose of this paper is to present a new approach for the creation of a framework for the recognition of ADL, analyzing the allowed sensors available in the mobile devices, and the existing methods available in the literature.

1. Introduction

Sensors embedded in off-the-shelf mobile devices, e.g., accelerometers, gyroscopes, magnetometers, microphones, and Global Positioning System (GPS) receivers [1], may be used in the development of algorithms for the recognition of Activities of Daily Living (ADL) [2] and the environments in which they are carried out. These algorithms are part of the development of a Personal Digital Life Coach (PDLC) [3]. According to [3], a PDLC “(…) will monitor our actions and activities, be able to recognize its user state of mind, and propose measures that not only will allow the user to achieve his/her stated goals, but also to act as an intermediate health and well-being agent between the user and his/her immediate care givers (…)”. This work is related to the development of ambient assisted living (AAL) systems, and, due to the increasing demands in our society, it is a field with high importance [4]. Due to recent advances in technology, there is an increasing number of research studies in this field for the monitoring of people with impairments and older people in a plethora of situations by using AAL technologies, including mobile devices and smart environments [5].
Multi-sensor data fusion technologies may be implemented with mobile devices, because they incorporate several sensors, such as motion sensors, magnetic/mechanical sensors, acoustic sensors, and location sensors [6], improving the accuracy of the recognition of several types of activities, e.g., walking, running, going downstairs, going upstairs, watching TV, and standing, and environments, e.g., bar, classroom, gym, library, kitchen, street, hall, living room, and bedroom. The selection of the activities and environments that will be included in the framework was based in the activities previously recognized with best accuracies, and, in the case of the environments, there are a lack of studies related to the environment recognition, taking into account some of the environments previously recognized and the most common environments [7]. The recognition of ADL may be performed with motion, magnetic/mechanical and location sensors, and the environments may be recognized with acoustic sensors. In order to improve the recognition of the ADL, the environment recognized may be fused with the other features extracted from the other sensors.
In accordance with previous works [6,8,9], the main motivation of this paper is to present the architecture of a framework for the recognition of ADL and their environments, which takes advantage of the use of a wide set of sensors available in a mobile device, also aiming at reducing the current complexity and constraints in the development of these systems. The test and validation of this framework is currently the subject of another step of this research plan [9], which includes the acquisition of a dataset that contains approximately 2.7 h of data collected from the accelerometer, gyroscope, magnetometer, microphone and GPS receiver, related to each activity and environment. During the collection phase, the data were acquired with the mobile device located in the front pocket of the trousers by 25 subjects aged between 16 and 60 years old and different lifestyles (10 mainly active and 15 mainly sedentary) and gender (10 female and 15 male). The activities performed and the environments frequented were labelled by the user. The subjects used their personal mobile phones with their applications running, where the mainly used device was a BQ Aquarius device [10].
The identification of ADL and environments using sensors has been studied during the last years, and several methods and frameworks [11,12,13,14,15,16] have been implemented using smartphones. However, this is a complex problem that should be separated into different stages, such as data acquisition, processing, and fusion; and artificial intelligence systems. The frameworks developed in previous studies are commonly only focused on some specific parts of the problem. For example, the Acquisition Cost-Aware QUery Adaptation (ACQUA) framework [17] has been designed for data acquisition and data processing, but it does not include all the steps needed for data processing.
There are no predefined standards for the creation of a framework for the recognition of the ADL [18,19,20], and the most implemented methods for the recognition of ADL are related to the use of motion sensors. However, there are methods and sensors that can be fused for the creation of a structured framework as a holistic approach to the identification of the ADL and environments presented in this paper.
Around the concept of sensors’ data fusion, the selection of the sensors to use is the first step for the creation of the framework, defining a method for the acquisition of the data, and, consequently, their processing. The processing of the data includes data cleaning, data imputation, and extraction of the features. Data segmentation techniques are not considered, as this study was designed for local execution on mobile devices and, due to the low memory and power processing restrictions of these devices, only a short sample of the sensors’ data can be used (initial research points to 5 s samples). This strategy makes it unsuitable to apply data segmentation techniques while still making it possible to deploy the framework in scarce resource devices. The final step in the proposed framework is the selection of the best features, and then the application of artificial intelligence techniques, i.e., the implementation of three types of Artificial Neural Networks (ANN), such as Multilayer Perceptron (MLP) with Backpropagation, Feedforward Neural Networks (FNN) with Backpropagation and Deep Neural Networks (DNN), in order to choose the best method for the accurate recognition of the ADL and the environments.
The remaining sections of this paper are organized as follows: Section 2 presents the state of the art in this topic, presenting a set of methods for each module/stage. Section 3 presents the framework for the identification of ADL using the sensors available in off-the-shelf mobile devices, the sensors and the methods that may be used. Section 4 presents a discussion and conclusions about the new approach proposed.

3. Methods and Expected Results

The new approach proposed for the creation of the framework for the identification of ADL (Figure 1) is based on [6,8,9], and it is composed by several stages. They are: the selection of the sensors, the data and processing, including data cleaning, imputation, and feature extraction, data fusion, the identification of ADL with artificial intelligence, including pattern recognition, and other machine learning techniques, and, at the end, the combination of the results obtained with the data available in the users’ agenda.
In order to create a new approach for a framework for the identification of ADL and their environments, the architecture, presented in Figure 1, and set of methods presented in Section 2 are proposed for obtaining results with reliable accuracy.
Following the list of sensors available in off-the-shelf mobile devices, presented in Section 2.1, the sensors that will be used in the framework should be dynamically selected, according to the sensors available in the mobile device. Thus, the types of sensors selected to use in the framework will be motion sensors, magnetic/mechanical sensors, acoustic sensors, and location sensors. The accelerometer is available in all mobile devices, but the gyroscope is only available on some devices, therefore, to cover the execution of the framework in all devices, two different methods should be implemented, one considering the data from the accelerometer and the gyroscope, and another considering only the data from the accelerometer. The magnetometer is only available on some devices, therefore this sensor should be managed similarly. Related to the acoustic sensors, the microphone is available in all mobile devices. As to the location sensors, the GPS is available in most of the mobile devices and its data should be used in the framework whenever possible.
The data acquisition methods are not directly related to the development of the framework, because the different manufacturers of the mobile operating systems have different methodologies to acquire the different types of sensors’ data. Thus, the data acquisition methods, presented in Section 2.2, should take in account the limitations of the mobile devices. Based on previous research studies and preliminary experiments, acquiring only 5 s of data from the selected sensors every 5 min is sufficient for the identification of the ADL and environments.
Following the creation of the new approach for a framework for the identification of ADL and their environments, the selection of data processing methods, presented in Section 2.3, should contain the data cleaning, data imputation, and feature extraction methods.
The data cleaning methods adapted for the framework depends on the types of sensors. On the one hand, for the accelerometer, gyroscope, and magnetometer sensors, the data cleaning method that should be applied is a low pass filter to remove the noise and the value of the gravity acquired during the data acquisition process. On the other hand, for the acoustic sensors, the data cleaning method that should be applied is the FFT in order to extract the frequencies of the audio. As the location sensors return values that are in nature already a result (e.g., GPS coordinates), data cleaning methods are not significant. Nevertheless, and as future work, it may be necessary to implement algorithms that increase the accuracy of these sensors as to better contribute to a quality data fusion process.
The data imputation methods is not important to implement in the development of a new approach for a framework for the identification of ADL and their environments, assuming that the data acquired from all sensors is always filled.
Related to the feature extraction, the features needed to recognize the ADL and their environments should be selected based on the type of sensors and on the selected features already reported in the literature and presented in Section 2.3.3. Firstly, the features selected for the accelerometer, gyroscope, and magnetometer sensors are the five greater distances between the maximum peaks, the average of the maximum peaks, the standard deviation of the maximum peaks, the variance of the maximum peaks, the median of the maximum peaks, the standard deviation of the raw signal, the average of the raw signal, the maximum value of the raw signal, the minimum value of the raw signal, the variance of the of the raw signal, and the median of the raw signal. Secondly, the features selected for the microphone are the standard deviation of the raw signal, the average of the raw signal, the maximum value of the raw signal, the minimum value of the raw signal, the variance of the of the raw signal, the median of the raw signal, and 26 MFCC coefficients. Finally, the features selected for the GPS receiver are the distance travelled during the acquisition time.
Before the presentation of the data fusion and pattern recognition methods that should be used for in the framework, the ADL and environments to recognize should be defined. This process should be executed with several sensors, that will be combined as presented in the Figure 2 and Table 7, being these the necessary stages:
Figure 2. Sensors used for the recognition of Activities of Daily Living (ADL) and environments for each phase of development.
Table 7. Sensors, Activities of Daily Living (ADL), and environments for recognition with the framework proposed.
  • Firstly, the ADL are recognized with motion and magnetic/mechanical sensors;
  • Secondly, the identification of the environments is performed with acoustic sensors;
  • Finally, there are two options, being these:
    ○
    The identification of standing activities with the fusion of the data acquired from motion and magnetic/mechanical sensors, and the environment recognized, where the number of ADL recognized depends on the number of sensors available;
    ○
    The identification of standing activities with the fusion of the data acquired from motion, magnetic/mechanical and location sensors, and the environment recognized, where the number of ADL recognized depends on the number of sensors available.
In identifying the environments, what is intended is to identify the associated activity, i.e., the sound generated in a classroom is not only the sound of the room itself, but rather the sound of a class who is having a lesson in a classroom. This is to say that an environment is to be considered as a place where some activity occurs in a given time of the day or the week, so there will be the need to consider different types of “Street” environments as they will have different audio signatures at different times of the day or week and of course, in different streets. All the proposed environments shown in Figure 2 are expected to be plural.
Firstly, the ADL to be identified with the framework will be going downstairs, going upstairs, running, walking, and standing, because they are part of the most recognized ADL in previous studies with reliable accuracy [7]. Secondly, the proposed environments to identify with the framework will be bar, classroom, gym, kitchen, library, street, hall, watching TV, and bedroom, because the existence of previous studies related to the recognition of environments is very limited, the proposed framework will take in account the most common environments and some of the environments previously recognized [7]. Thirdly, the proposed ADL to distinct with the framework will be sleeping, and standing, because the ADL may be confused as standing ADL and the inclusion of the environment recognized as an input for the classification method will help in the accurate recognition of these ADL. Finally, the proposed ADL to distinct with the framework are sleeping, standing, and driving, because the driving may also confused as standing ADL and, in order to accurately distinct these ADL, the environment recognized and the features extracted from the GPS receiver should be included. As the data for the creation of the methods for the recognition of ADL and environments was acquired will several conditions and different people, the generated method with ANN will be generic and the calibration of sensor is not needed.
Based on the list of data fusion methods and pattern recognition methods, defined in Section 2.4 and Section 2.5, the method selected for the implementation in the new approach for a framework for the identification of ADL and their environments will be based in ANN methods, because, based on the literature, it is one of the methods that reports the best accuracies. However, the selection of the best type of ANN will be done with the comparison of the results obtained with three types of ANN selected. The types of ANN that will be tested to the acquired data are:
  • MLP with Backpropagation;
  • FNN with Backpropagation;
  • DNN.
Regarding the data acquired from GPS receiver, it can be useful to increase the accuracy of the identification of the ADL and their environments, but it can also be used for the identification of the location where the ADL are executed, in order to improve the comparison with the users’ agenda presented in Section 2.6.

4. Discussion and Conclusions

This paper presents the architecture of a new approach for a framework for the identification of ADL and their environments, using methods with a reported good accuracy. The development of the new approach for the development of a framework for the identification of ADL and their environments, based on the system presented in [6,8,9], is one of the steps for the creation of a personal digital life coach [3] using mobile devices.
The framework will be composed by several modules several, such as data acquisition, data processing, data fusion, and a module to implement artificial intelligence techniques for the identification of the ADL and their environments.
The sensors used in the framework will be accelerometer, gyroscope, magnetometer, microphone, and GPS receiver, in order to recognize several ADL, including going downstairs, going upstairs, running, walking, standing, sleeping, and driving, and their environments, including bar, classroom, gym, kitchen, library, street, hall, watching TV, and bedroom.
The sensors’ data should be acquired and, before the extraction of the features of the sensors’ data, filters such as low pass filter and FFT, should be applied. Afterwards, the data fusion and pattern recognition methods should be applied for the recognition of ADL and environments.
This paper consists on a conceptual definition of the framework for the recognition of the ADL and their environments, proposing three possible methods for this purpose, based on the use of the ANN methods. In order to define the best method, the future implementation of the proposed methods will compare the differences between them, including the accuracy, performance, and adaptability for the development of a local processing framework for mobile devices. It will include the acquisition of a large set of sensors’ data related to the ADL and environments proposed for the creation of training and testing sets and further validation of the developed methods. Additionally, and also as future work, the framework will allow each user to validate the ADL identified by the framework when this is not the real performed activity.
Due to the inexistence of previous studies that review the use of all sensors available in current off-the-shelf mobile devices, our proposed framework is a function of the number of sensors available in the mobile device used, proving a reliable feedback in almost real-time.

Acknowledgments

This work was supported by FCT project UID/EEA/50008/2013. The authors would also like to acknowledge the contribution of the COST Action IC1303–AAPELE–Architectures, Algorithms and Protocols for Enhanced Living Environments.

Author Contributions

All the authors have contributed with the structure, content, and writing of the paper.

Conflicts of Interest

The authors declare no conflict of interest.

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