Novel approaches to human activity recognition based on accelerometer data

Jordão, Artur; Tôrres, Leonardo A. B.; Schwartz, William Robson

doi:10.1007/s11760-018-1293-x

Cited by 52 publications

(41 citation statements)

References 26 publications

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“…The acceleration plots for the Six Activities (a-f) shown below in Figure 9 represents 9,16] which is quite unique. These observations can be proven by Table 3 and Figure 10 depicting a combination of the different axis of the accelerometer to recognize the activities.…”

Section: Axis Analysis Of Activitiesmentioning

confidence: 99%

“…Non-obtrusive sensors are used in smart homes and smartphones. In smart homes, different motion and door sensors are installed at different locations and the primary objective is to recognize and assess activities but in smart homes, physical activities (i.e., running, cycling) cannot be performed due to the nature of activities.The most widely used sensors for recording physical activities data using a smartphone are the accelerometer, gyroscope, and position sensor [8,[14][15][16][17][18][19][20]]. An accelerometer is capable of tracking activity readings to infer complex user motions, such as tilt, swing, or rotation.Researchers showed that the accelerometer sensor is the most reliable and cheapest alternate of wearable sensors for physical activity recognition [19,[21][22][23].…”

mentioning

confidence: 99%

“…The most widely used sensors for recording physical activities data using a smartphone are the accelerometer, gyroscope, and position sensor [8,[14][15][16][17][18][19][20]]. An accelerometer is capable of tracking activity readings to infer complex user motions, such as tilt, swing, or rotation.…”

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confidence: 99%

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Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Javed

Sarwar

Khan

et al. 2020

Sensors

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Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods.2 of 18 emotional issues, and depression as well. Physical fitness can be tracked and analyzed by monitoring daily life physical activities.Physical activity recognition was initiated back in 2004 using on-body sensors. Researchers in [2] used the accelerometer's annotated data to recognize the physical activities. They made an Android-based system that collects raw data from the accelerometer and applied machine learning algorithms to predict physical activities. Authors in [3] recognized six basic activities, i.e., walking, jogging, sitting, standing, upstairs and downstairs. Authors in [4] used on-body sensors for activity recognition but found that it is very difficult to carry the sensors all the time. Many authors suggested that a smartphone is a non-obtrusive option for activity recognition [3,[5][6][7][8][9].The smartphone is playing a vital role in modern life. It provides services and applications such as health monitoring, early-stage disease detection, sports analysis, fitness tracking, and behavior analysis. Android-based smartphones have a built-in motion sensor that provides accurate and precise acceleration readings against physical activities. In early conditions, dedicated sensors were used for activity recognition. There exist several techniques for physical activity recognition such as on-body obtrusive and non-obtrusive sensors [10][11][12][13]. Non-obtrusive sensors are used in smart homes and smartphones. In smart homes, different motion and door sensors are installed at different locations and the primary objective is to recognize...

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Section: Axis Analysis Of Activitiesmentioning

confidence: 99%

mentioning

confidence: 99%

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Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Javed

Sarwar

Khan

et al. 2020

Sensors

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“…Finally, the optimal number and combination of four sensors were obtained to classify four actions using a set of features from acceleration, angular velocity, and SVM. 32 In most of these works, data were captured under controlled conditions, [27][28][29]31 and the rest in free-living conditions. 26,30,32 Few studies have been identified in which high-level data are used, and all of them use data from IMUs.…”

Section: Related Workmentioning

confidence: 99%

Human action recognition based on low- and high-level data from wearable inertial sensors

López-Nava

Muñoz-Meléndez

2019

International Journal of Distributed Sensor Networks

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Human action recognition supported by highly accurate specialized systems, ambulatory systems, or wireless sensor networks has a tremendous potential in the areas of healthcare or wellbeing monitoring. Recently, several studies carried out focused on the recognition of actions using wearable inertial sensors, in which raw sensor data are used to build classification models, and in a few of them high-level representations are obtained which are directly related to anatomical characteristics of the human body. This research focuses on classifying a set of activities of daily living, such as functional mobility, and instrumental activities of daily living, such as preparing meals, performed by test subjects in their homes in naturalistic conditions. The joint angles of upper and lower limbs are estimated using information from five wearable inertial sensors placed on the body of five test subjects. A set of features related to human limb motions is extracted from the orientation signals (high-level data) and another set from the acceleration raw signals (low-level data) and both are used to build classifiers using four inference algorithms. The proposed features in this work are the number of movements and the average duration of consecutive movements. The classifiers are capable of successfully classifying the set of actions using raw data with up to 77.8% and 93.3% from high-level data. This study allowed comparing the use of two data levels to classify a set of actions performed in daily environments using an inertial sensor network.

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“…Many published papers addressed the issue of modelling human behaviour using wearable sensors [24,25]. Developing activity recognition systems using the smartphones' built-in accelerometer together with employing CNNs to model the activities was addressed in some recent publications [26,27].…”

Section: Literature Reviewmentioning

confidence: 99%

Human Activities Recognition Based on Neuro-Fuzzy Finite State Machine

2018

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Human activity recognition and modelling comprise an area of research interest that has been tackled by many researchers. The application of different machine learning techniques including regression analysis, deep learning neural networks, and fuzzy rule-based models has already been investigated. In this paper, a novel method based on Fuzzy Finite State Machine (FFSM) integrated with the learning capabilities of Neural Networks (NNs) is proposed to represent human activities in an intelligent environment. The proposed approach, called Neuro-Fuzzy Finite State Machine (N-FFSM), is able to learn the parameters of a rule-based fuzzy system, which processes the numerical input/output data gathered from the sensors and/or human experts’ knowledge. Generating fuzzy rules that represent the transition between states leads to assigning a degree of transition from one state to another. Experimental results are presented to demonstrate the effectiveness of the proposed method. The model is tested and evaluated using a dataset collected from a real home environment. The results show the effectiveness of using this method for modelling the activities of daily living based on ambient sensory datasets. The performance of the proposed method is compared with the standard NNs and FFSM techniques.

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Novel approaches to human activity recognition based on accelerometer data

Cited by 52 publications

References 26 publications

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Human action recognition based on low- and high-level data from wearable inertial sensors

Human Activities Recognition Based on Neuro-Fuzzy Finite State Machine

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