Feature Selection for Wearable Smartphone-Based Human Activity Recognition with Able bodied, Elderly, and Stroke Patients

Capela, Nicole A.; Lemaire, Edward D.; Baddour, Natalie

doi:10.1371/journal.pone.0124414

Cited by 162 publications

(118 citation statements)

References 23 publications

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“…Capela et al [43] identified seven different meta-classes (or levels) of activities differing by the level of detail: Level 1: Mobile, and immobile (large movements and stairs labeled as mobile; sit, stand, lie, and small movements labeled as immobile); Level 2: Sit, and stand (not including small movements); Level 3: Sit, stand, and lie; Level 4: Large movements (going upstairs); Level 5: Ramp up, ramp down, large movements, stairs up, and stairs down; Level 6: Small movements (e.g., sitting, standing or lying); and Level 7: Transition states (transition between activities).…”

Section: Taxonomy Of Human Activitiesmentioning

confidence: 99%

“…Based on the extensive analysis of literature and features used by other authors (especially by Capela et al [43], Mathie et al [53], Zhang and Sawchuk [52]), we have extracted 99 features of data, which have been detailed in [54]. The feature ranking was performed using Kullback-Leibler divergence as class separability criterion on the human activity data from the USC-HAD dataset as described in [54].…”

Section: Featuresmentioning

confidence: 99%

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Smartphone User Identity Verification Using Gait Characteristics

et al. 2016

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Smartphone-based biometrics offers a wide range of possible solutions, which could be used to authenticate users and thus to provide an extra level of security and theft prevention. We propose a method for positive identification of smartphone user's identity using user's gait characteristics captured by embedded smartphone sensors (gyroscopes, accelerometers). The method is based on the application of the Random Projections method for feature dimensionality reduction to just two dimensions. Then, a probability distribution function (PDF) of derived features is calculated, which is compared against known user PDF. The Jaccard distance is used to evaluate distance between two distributions, and the decision is taken based on thresholding. The results for subject recognition are at an acceptable level: we have achieved a grand mean Equal Error Rate (ERR) for subject identification of 5.7% (using the USC-HAD dataset). Our findings represent a step towards improving the performance of gait-based user identity verification technologies.

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Section: Taxonomy Of Human Activitiesmentioning

confidence: 99%

Section: Featuresmentioning

confidence: 99%

Smartphone User Identity Verification Using Gait Characteristics

et al. 2016

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“…A further pre-processing of extracted features is needed to deal with the issue of features heterogeneity before classification. This is done through feature normalisation which is often applied in many machine learning applications (Sung et al 2012;Capela et al 2015). Normalisation of each feature in the activity features matrix obtained in Eq.…”

Section: Feature Normalisationmentioning

confidence: 99%

“…RGB-D sensors) (Sung et al 2011(Sung et al , 2012Han et al 2017) and non-visual sensors (e.g. wearable sensors) (Capela et al 2015) which make it a lot easier to obtain information of activities. Although non-visual sensors have certain advan-…”

Section: Introductionmentioning

confidence: 99%

Human activity learning for assistive robotics using a classifier ensemble

et al. 2018

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Assistive robots in ambient assisted living environments can be equipped with learning capabilities to effectively learn and execute human activities. This paper proposes a human activity learning (HAL) system for application in assistive robotics. An RGB-depth sensor is used to acquire information of human activities, and a set of statistical, spatial and temporal features for encoding key aspects of human activities are extracted from the acquired information of human activities. Redundant features are removed and the relevant features used in the HAL model. An ensemble of three individual classifiers-support vector machines (SVMs), K -nearest neighbour and random forest-is employed to learn the activities. The performance of the proposed system is improved when compared with the performance of other methods using a single classifier. This approach is evaluated on experimental dataset created for this work and also on a benchmark dataset-the Cornell Activity Dataset (CAD-60). Experimental results show the overall performance achieved by the proposed system is comparable to the state of the art and has the potential to benefit applications in assistive robots for reducing the time spent in learning activities.

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“…The respective authors proposed two different filter strategies, namely CFS and Relief-f feature selection in recognizing the construction activity. Challita et In other works, Capela et al (2015) used Relief-f, CFS, and fast correlation methods to recognize human activity from three different types of users, namely able-bodied, elderly, and stroke patients. On the other hand, an evolutionary algorithm (EA) also gained an attention among researchers in feature reduction.…”

Section: Related Workmentioning

confidence: 99%

Recognizing Complex Human Activities using Hybrid Feature Selections based on an Accelerometer Sensor

Zainudin¹,

Sulaiman²,

Mustapha³

et al. 2017

IJTech

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Wearable sensor technology is evolving in parallel with the demand for human activity monitoring applications. According to World Health Organization (WHO), the percentage of health problems occurring in the world population, such as diabetes, heart problem, and high blood pressure rapidly increases from year-to-year. Hence, regular exercise, at least twice a week, is encouraged for everyone, especially for adults and the elderly. An accelerometer sensor is preferable, due to privacy concerns and the low cost of installation. It is embedded within smartphones to monitor the amount of physical activity performed. One of the limitations of the various classifications is to deal with the large dimension of the feature space. Practically speaking, a large amount of memory space is demanded along with high processor performance to process a large number of features. Hence, the dimension of the features is required to be minimized by selecting the most relevant feature before it is classified. In order to tackle this issue, the hybrid feature selection using Relief-f and differential evolution is proposed. The public domain activity dataset from Physical Activity for Ageing People (PAMAP2) is used in the experimentation to identify the quality of the proposed method. Our experimental results show outstanding performance to recognize different types of physical activities with a minimum number of features. Subsequently, our findings indicate that the wrist is the best sensor placement to recognize the different types of human activity. The performance of our work also been compared with several state-of-the-art of features for selection algorithms.

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Feature Selection for Wearable Smartphone-Based Human Activity Recognition with Able bodied, Elderly, and Stroke Patients

Cited by 162 publications

References 23 publications

Smartphone User Identity Verification Using Gait Characteristics

Smartphone User Identity Verification Using Gait Characteristics

Human activity learning for assistive robotics using a classifier ensemble

Recognizing Complex Human Activities using Hybrid Feature Selections based on an Accelerometer Sensor

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