Segmenting human activities based on HMMs using smartphone inertial sensors

San-Segundo, Rubén; Lorenzo-Trueba, Jaime; Martínez-González, Beatriz; Pardo, José Manuel

doi:10.1016/j.pmcj.2016.01.004

Cited by 45 publications

(26 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recall is defined as the time correctly assigned (true positive) divided by the actual duration of the activity. As it was shown in [17], there is an important correlation between these three measures. Because of this, ARER will be considered for system development, and in the final experiments, all metrics will be provided for comparison with further works.…”

Section: Dataset Used In the Experimentsmentioning

confidence: 71%

“…These devices have important advantages [11,12]: easy device portability, unobtrusive sensing provided by the embedded sensors and the processing power of new smartphones that allow online computation. Because of this, some works focused on HAR using smartphones have been developed [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…This meta-classifier is trained with user-dependent adaptation data to improve the results for a specific user. Figure 1 shows the general system architecture presented in [17], including an additional module for user MAP adaptation. The system is made up of four main modules: feature extraction, HMMs training, HMMs adaptation and activity recognition/segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…The main contributions of this paper are focused on HMMs training and adaptation modules. In order to consider only the influence of the HMMs in the results, the Activity Sequence Model (ASM) proposed in [17] has been deactivated in this work.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

HMM Adaptation for Improving a Human Activity Recognition System

et al. 2016

Self Cite

View full text Add to dashboard Cite

When developing a fully automatic system for evaluating motor activities performed by a person, it is necessary to segment and recognize the different activities in order to focus the analysis. This process must be carried out by a Human Activity Recognition (HAR) system. This paper proposes a user adaptation technique for improving a HAR system based on Hidden Markov Models (HMMs). This system segments and recognizes six different physical activities (walking, walking upstairs, walking downstairs, sitting, standing and lying down) using inertial signals from a smartphone. The system is composed of a feature extractor for obtaining the most relevant characteristics from the inertial signals, a module for training the six HMMs (one per activity), and the last module for segmenting new activity sequences using these models. The user adaptation technique consists of a Maximum A Posteriori (MAP) approach that adapts the activity HMMs to the user, using some activity examples from this specific user. The main results on a public dataset have reported a significant relative error rate reduction of more than 30%. In conclusion, adapting a HAR system to the user who is performing the physical activities provides significant improvement in the system's performance.

show abstract

Section: Dataset Used In the Experimentsmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

HMM Adaptation for Improving a Human Activity Recognition System

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…To deal with this problem, the HAR system must extract features from sensor signals, generate a model for each activity, and classify next activities based on these models. In the literature, different machine learning solutions have been applied to the recognition of activities including Naive Bayes [3], Decision Trees [4], Support Vector Machines (SVMs) [5], Deep Neural Networks [6] and Hidden Markov Models (HMMs) [7]. In many works, several approaches have been compared using the WEKA learning toolkit [8] because it incorporates many machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction for robust physical activity recognition

Zhu

San-Segundo

Pardo

2017

Hum. Cent. Comput. Inf. Sci.

Self Cite

View full text Add to dashboard Cite

IntroductionWith information obtained from sensors, computer based system can make more intelligent actions by adapting their behavior to the context conditions. These days, thanks to the development of multi-sensor networks, related research areas have increased rapidly. Among those areas, Human Activity Recognition (HAR) based on wearable sensors (accelerometer, gyroscope, magnetometer, etc.) has recently received lots of attention due to its large number of promising applications. One of the most interesting HAR applications is ubiquitous identification of physical activity. As we know, over-weighting is a general human problem as a result of a physical inactivity habit. A Lancet publication [1] estimates that physical inactivity causes 9% of all premature deaths worldwide. By monitoring physical activity, we can help people to learn the calories they consumed or gained during the day in a much more precise way and Abstract This paper presents the development of a Human Activity Recognition (HAR) system that uses a network of nine inertial measurement units situated in different body parts. Every unit provides 3D (3-dimension) acceleration, 3D angular velocity, 3D magnetic field orientation, and 4D quaternions. This system identifies 33 different physical activities (walking, running, cycling, lateral elevation of arms, etc.). The system is composed of two main modules: a feature extractor for obtaining the most relevant characteristics from the inertial signals every second, and a machine learning algorithm for classifying between the different activities. This paper focuses on the feature extractor module, evaluating several types of features and proposing different normalization approaches. This paper also analyses the performance of every sensor included in the inertial measurement units. The main experiments have been done using a public available dataset named REALDISP Activity Recognition dataset. This dataset includes recordings from 17 subjects performing 33 different activities in three different scenarios. Final results demonstrate that the proposed HAR system significantly improves the classification accuracy compared to previous works on this dataset. For the best configuration, the system accuracy is 99.1%. This system has been also evaluated with the OPPORTUNITY dataset obtaining competitive results.

show abstract

Cyberspace for Smart Parenting with Sensors

Prusty

2019

Security Designs for the Cloud, Iot, and Social Networking

View full text Add to dashboard Cite

Segmenting human activities based on HMMs using smartphone inertial sensors

Cited by 45 publications

References 36 publications

HMM Adaptation for Improving a Human Activity Recognition System

HMM Adaptation for Improving a Human Activity Recognition System

Feature extraction for robust physical activity recognition

Cyberspace for Smart Parenting with Sensors

Contact Info

Product

Resources

About