Personalized Real Time Human Activity Recognition

Gadebe, Moses L.; Kogeda, Okuthe P.; Ojo, Sunday O.

doi:10.1109/iscmi.2018.8703240

Cited by 5 publications

(8 citation statements)

References 15 publications

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“…Each of 51 collected files consist of 6 attributes (subject code, activity label, time-stamp, X, Y and Z tri-axial values) and partitioned as (Non-hand-oriented activities: {walking, jogging, stairs, standing, kicking}, Hand-oriented activities (General): {dribbling, playing catch, typing, writing, clapping, brushing teeth, folding clothes} and Hand-oriented activities (eating): {eating pasta, eating soup, eating sandwich, eating chips, drinking}). However, it lacks features capable to capture bodily postures to mimic walking patterns completely [10]. Similar to our study in [9] is the work of [15], they found that learning new activities to adapt to new users' needs is challenging due to shortage of annotated dataset.…”

Section: Related Worksupporting

confidence: 53%

“…In this process, we present sensory data collection using cost effective Smartphone accelerometer available through the day at closer proximity of subjects [10]. All subjects are expected to carry Smartphone inside front pocket similar to [13]…”

Section: Sensory Data Collection Processmentioning

confidence: 99%

“…In this process, we solve Smartphone orientation (Rest or Portrait or Landscape) to extract signal values using signal magnitude vector (SMV) and TA, because SMV is inadequate to capture different bodily postures [9] [10]. Based on size of arc due to restoring gravity force at the time we determine Smartphone orientation.…”

Section: Pre-processingmentioning

confidence: 99%

“…Therefore, if gravimeter approximates to 9.8 𝑚/𝑠 2 as g π 2 ≈ 1g (0.994), x and y extremes positions of harmonic motion are sinusoidal in time t (period) computed by equation ( 5) and ( 6) [10].…”

Section: Pre-processingmentioning

confidence: 99%

“…However, most of the dataset lacks a common feature to completely capture bodily posture to accurately classify static and dynamic human activities, hence subject-sensitivity challenge still persist. In this paper, we present a compressed top-k personalized dataset based on harmonic motion principle to mimic walking transition to augment Signal Magnitude Vector and Tilt Angle proposed in [9] and [10]. In this paper our contribution is three-folds, firstly a novel gravimeter filtering technique to select usable best classification feature.…”

Section: Introductionmentioning

confidence: 99%

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Top-K Human Activity Recognition Dataset

Gadebe

Kogeda

2020

Int. J. Interact. Mob. Technol.

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<span lang="EN-US">The availability of Smartphones has increased the possibility of self-monitoring to increase physical activity and behavior change to prevent obesity. However self-monitoring on a Smartphtone comes with some challenges such as unavailability of lightweight classification algorithm, personalized dataset to completely capture bodily postures, subject sensitivity, limited storage and computational power. However, most classification algorithms such as Support Vector Machines, C4.5, Naïve Bayes and K Neighbor relies on larger dataset to accurately predict human activities. In this paper, we present top-k of compressed small personalized dataset to reduce computational cost with increased accuracy. We collected top-k personalized dataset from 13 recruited subjects. After benchmarking our collected dataset we found that the dataset is suitable for tree-oriented algorithm, especially the Random Forest, C4.5 and Boosted tree with accuracy and precision of 100% except for KNN, Support Vector and Naïve Bayes. Further, our top-k personalized dataset improves pruning and overfitting of tree-oriented algorithms. Moreover, the linear consistence of static human activities reveals the potential of our top-k dataset to be replicated to multiple-subject to close subject sensitivity challenge.</span>

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Section: Related Worksupporting

confidence: 53%

Section: Sensory Data Collection Processmentioning

confidence: 99%