Stability Analysis of the t-SNE Algorithm for Human Activity Pattern Data

Hamad, Rebeen Ali; Järpe, Eric; Lundström, Jens

doi:10.1109/smc.2018.00318

Cited by 14 publications

(7 citation statements)

References 25 publications

(24 reference statements)

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“…The results of the proposed network for HAR from wearable sensors data are compared with the results of the existing methods. Tables 11,12 and 13 show the detailed results of our proposed network compared with the existing methods. The results of the proposed network from smartphone sensors data are shown in Table 11.…”

Section: Results From Wearable Sensors Datasetsmentioning

confidence: 99%

“…Sensors from smart home environments and wearable objects generate a large amount of valuable data used for different applications including human activity recognition (HAR). Smart home environments based on equipped sensors are designed for ambient assisted living to unobtrusively track human activities [12]. Further, wearable sensors have been also used to gather customized data about users' habits.…”

Section: Introductionmentioning

confidence: 99%

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ConvNet-based performers attention and supervised contrastive learning for activity recognition

et al. 2022

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Human activity recognition based on generated sensor data plays a major role in a large number of applications such as healthcare monitoring and surveillance system. Yet, accurately recognizing human activities is still challenging and active research due to people’s tendency to perform daily activities in a different and multitasking way. Existing approaches based on the recurrent setting for human activity recognition have some issues, such as the inability to process data parallelly, the requirement for more memory and high computational cost albeit they achieved reasonable results. Convolutional Neural Network processes data parallelly, but, it breaks the ordering of input data, which is significant to build an effective model for human activity recognition. To overcome these challenges, this study proposes causal convolution based on performers-attention and supervised contrastive learning to entirely forego recurrent architectures, efficiently maintain the ordering of human daily activities and focus more on important timesteps of the sensors’ data. Supervised contrastive learning is integrated to learn a discriminative representation of human activities and enhance predictive performance. The proposed network is extensively evaluated for human activities using multiple datasets including wearable sensor data and smart home environments data. The experiments on three wearable sensor datasets and five smart home public datasets of human activities reveal that our proposed network achieves better results and reduces the training time compared with the existing state-of-the-art methods and basic temporal models.

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Section: Results From Wearable Sensors Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ConvNet-based performers attention and supervised contrastive learning for activity recognition

et al. 2022

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“…The real environment is the Halmstad Intelligent Home (HINT) located in Halmstad, Sweden [ 58 , 59 ], which is supplied with contact/touch sensors recognizing occupancy in chairs and sofas. This apartment is also provided with Passive Infrared (PIR) sensors located in different spaces, thereby facilitating the continuous monitoring of motion and occupancy of subjects staying in their settings.…”

Section: Experiments Definitionmentioning

confidence: 99%

Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients

Ortíz‐Barrios

Järpe

García-Constantino

et al. 2022

Sensors

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The accurate recognition of activities is fundamental for following up on the health progress of people with dementia (PwD), thereby supporting subsequent diagnosis and treatments. When monitoring the activities of daily living (ADLs), it is feasible to detect behaviour patterns, parse out the disease evolution, and consequently provide effective and timely assistance. However, this task is affected by uncertainties derived from the differences in smart home configurations and the way in which each person undertakes the ADLs. One adjacent pathway is to train a supervised classification algorithm using large-sized datasets; nonetheless, obtaining real-world data is costly and characterized by a challenging recruiting research process. The resulting activity data is then small and may not capture each person’s intrinsic properties. Simulation approaches have risen as an alternative efficient choice, but synthetic data can be significantly dissimilar compared to real data. Hence, this paper proposes the application of Partial Least Squares Regression (PLSR) to approximate the real activity duration of various ADLs based on synthetic observations. First, the real activity duration of each ADL is initially contrasted with the one derived from an intelligent environment simulator. Following this, different PLSR models were evaluated for estimating real activity duration based on synthetic variables. A case study including eight ADLs was considered to validate the proposed approach. The results revealed that simulated and real observations are significantly different in some ADLs (p-value < 0.05), nevertheless synthetic variables can be further modified to predict the real activity duration with high accuracy (R2(pred)>90%).

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“…In healthcare monitoring, HAR, as one of the significant applications of intelligent environment and wearable sensor technologies, has been used to monitor the activity of daily living (ADL) in order to support and assist senior people, disabled and cognitive impaired [4]. HAR from smart home setting equipped by ubiquitous sensors in the field of ambient assisted living has gained increased attention for improving the quality of independent living of the residents within the smart home environment [5]. Smart homes with unobtrusive sensing technology for HAR have been used as a suitable solution for enhancing independent living when privacy is concerned [4,6].…”

Section: Introductionmentioning

confidence: 99%

Dilated causal convolution with multi-head self attention for sensor human activity recognition

Hamad

Kimura²,

Yang

et al. 2021

Neural Comput & Applic

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Systems of sensor human activity recognition are becoming increasingly popular in diverse fields such as healthcare and security. Yet, developing such systems poses inherent challenges due to the variations and complexity of human behaviors during the performance of physical activities. Recurrent neural networks, particularly long short-term memory have achieved promising results on numerous sequential learning problems, including sensor human activity recognition. However, parallelization is inhibited in recurrent networks due to sequential operation and computation that lead to slow training, occupying more memory and hard convergence. One-dimensional convolutional neural network processes input temporal sequential batches independently that lead to effectively executed operations in parallel. Despite that, a one-dimensional Convolutional Neural Network is not sensitive to the order of the time steps which is crucial for accurate and robust systems of sensor human activity recognition. To address this problem, we propose a network architecture based on dilated causal convolution and multi-head self-attention mechanisms that entirely dispense recurrent architectures to make efficient computation and maintain the ordering of the time steps. The proposed method is evaluated for human activities using smart home binary sensors data and wearable sensor data. Results of conducted extensive experiments on eight public and benchmark HAR data sets show that the proposed network outperforms the state-of-the-art models based on recurrent settings and temporal models.

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Stability Analysis of the t-SNE Algorithm for Human Activity Pattern Data

Cited by 14 publications

References 25 publications

ConvNet-based performers attention and supervised contrastive learning for activity recognition

ConvNet-based performers attention and supervised contrastive learning for activity recognition

Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients

Dilated causal convolution with multi-head self attention for sensor human activity recognition

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