High-Level Features for Recognizing Human Actions in Daily Living Environments Using Wearable Sensors

López-Nava, Irvin Hussein; Muñoz-Meléndez, Angélica

doi:10.3390/proceedings2191238

Cited by 3 publications

(3 citation statements)

References 32 publications

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“…The complete description of the high-level features extraction is detailed in a previous study. 4 In brief, the algorithm search tendencies in the signals L that is related to the anatomical terms of movements in w i , such as the number of flexions of the shoulder or the number of extensions of the knee, and constitute the set of features hl f 1 .…”

Section: Methodsmentioning

confidence: 99%

“…Automatic recognition of human actions in naturalistic conditions, principally using wearable sensors, is still an open research problem of the field of pervasive computing. 4 Currently, action recognition helps at providing information about the behavior and habits of users that enable computing systems to assist users with their daily tasks. 5 Some of the advantages of using wearable sensors over video systems for capturing human motion are the robustness to occlusion and to changes in lighting and parallel that, they are portable.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Activity recognition is one of the most active areas of research in ubiquitous computing with applications in behavior change, risk prediction and early diagnosis [ 1 ]. Some of its main challenges include [ 2 ]: (i) the fact that human activities, especially human movement, present high intra-and inter-subject variability, and (ii) the daily dynamics of people in complex environments. Most of the works on activity recognition have started investigating from a general outlook, i.e., they focus on classifying activities that are very different from each other, such as walking vs. drinking coffee, instead of a particular, such as walking vs. running [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Gait Activity Classification on Unbalanced Data from Inertial Sensors Using Shallow and Deep Learning

López-Nava

Valentín-Coronado

García-Constantino

et al. 2020

Sensors

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Activity recognition is one of the most active areas of research in ubiquitous computing. In particular, gait activity recognition is useful to identify various risk factors in people’s health that are directly related to their physical activity. One of the issues in activity recognition, and gait in particular, is that often datasets are unbalanced (i.e., the distribution of classes is not uniform), and due to this disparity, the models tend to categorize into the class with more instances. In the present study, two methods for classifying gait activities using accelerometer and gyroscope data from a large-scale public dataset were evaluated and compared. The gait activities in this dataset are: (i) going down an incline, (ii) going up an incline, (iii) walking on level ground, (iv) going down stairs, and (v) going up stairs. The proposed methods are based on conventional (shallow) and deep learning techniques. In addition, data were evaluated from three data treatments: original unbalanced data, sampled data, and augmented data. The latter was based on the generation of synthetic data according to segmented gait data. The best results were obtained with classifiers built with augmented data, with F-measure results of 0.812 (σ = 0.078) for the shallow learning approach, and of 0.927 (σ = 0.033) for the deep learning approach. In addition, the data augmentation strategy proposed to deal with the unbalanced problem resulted in increased classification performance using both techniques.

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User-adaptive models for activity and emotion recognition using deep transfer learning and data augmentation

Garcia-Ceja

Riegler

Kvernberg

et al. 2019

User Model User-Adap Inter

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Building predictive models for human-interactive systems is a challenging task. Every individual has unique characteristics and behaviors. A generic human-machine system will not perform equally well for each user given the between-users differences. Alternatively, a system built specifically for each particular user will perform closer to the optimum. However, such a system would require more training data for every specific user, thus, hindering its applicability for real world scenarios. Collecting training data can be time consuming and expensive. For example, in clinical applications it can take weeks or months until enough data is collected to start training machine learning models. End users expect to start receiving quality feedback from a given system as soon as possible without having to rely on time consuming calibration and training procedures. In this work we build and test user adaptive models (UAM) which are predictive models that adapt to each users' characteristics and behaviors with reduced training data. Our UAMs are trained using deep transfer learning and data augmentation and were tested on two public datasets. The first one, is an activity recognition dataset from accelerometer data. The second one, is an emotion recognition dataset from speech recordings. Our results show that the UAMs have a significant increase in recognition performance with reduced training data with respect to a general model. Furthermore, we show that individual characteristics such as gender can influence the models' performance.

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High-Level Features for Recognizing Human Actions in Daily Living Environments Using Wearable Sensors

Cited by 3 publications

References 32 publications

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

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

Gait Activity Classification on Unbalanced Data from Inertial Sensors Using Shallow and Deep Learning

User-adaptive models for activity and emotion recognition using deep transfer learning and data augmentation

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