Opportunity++: A Multimodal Dataset for Video- and Wearable, Object and Ambient Sensors-Based Human Activity Recognition

We present a benchmark dataset for evaluating physical human activity recognition methods from wrist-worn sensors, for the specific setting of basketball training, drills, and games. Basketball activities lend themselves well for measurement by wrist-worn inertial sensors, and systems that are able to detect such sport-relevant activities could be used in applications of game analysis, guided training, and personal physical activity tracking. The dataset was recorded from two teams in separate countries (USA and Germany) with a total of 24 players who wore an inertial sensor on their wrist, during both a repetitive basketball training session and a game. Particular features of this dataset include an inherent variance through cultural differences in game rules and styles as the data was recorded in two countries, as well as different sport skill levels since the participants were heterogeneous in terms of prior basketball experience. We illustrate the dataset’s features in several time-series analyses and report on a baseline classification performance study with two state-of-the-art deep learning architectures.

Section: Discussionmentioning

confidence: 99%

Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors

Hoelzemann

Romero

Bock

et al. 2023

“…The software Matlab, hardware Intel Core i7 at 2.4 GHz along with 24 GB of RAM are utilized to assess the proposed system. Two indoor activities-based datasets known as HWU-USP [ 33 ] and Opportunity++ [ 34 ] are utilized for system performance measurement. A 10-fold cross-validation technique has been used over the dataset for training purposes.…”

Section: Experimental Setup and Evaluationmentioning

confidence: 99%

“…Whereas the ambient sensors, including 13 switches and 8 3D acceleration sensors, were attached to objects like milk, spoons, water bottles, glasses, drawers, and doors. Videos were recorded at 640 × 480 pixels and 10fps [ 34 ]. Figure 11 presents a few sample images captured during video recordings where a subject is (a) cleaning the table; (b) opening the door; (c) opening the drawer; (d) closing the door; (e) opening the door; and (f) closing the dishwasher.…”

Section: Experimental Setup and Evaluationmentioning

confidence: 99%

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A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network

Mudawi

Alabduallah

Jalal

et al. 2023

Locomotion prediction for human welfare has gained tremendous interest in the past few years. Multimodal locomotion prediction is composed of small activities of daily living and an efficient approach to providing support for healthcare, but the complexities of motion signals along with video processing make it challenging for researchers in terms of achieving a good accuracy rate. The multimodal internet of things (IoT)-based locomotion classification has helped in solving these challenges. In this paper, we proposed a novel multimodal IoT-based locomotion classification technique using three benchmarked datasets. These datasets contain at least three types of data, such as data from physical motion, ambient, and vision-based sensors. The raw data has been filtered through different techniques for each sensor type. Then, the ambient and physical motion-based sensor data have been windowed, and a skeleton model has been retrieved from the vision-based data. Further, the features have been extracted and optimized using state-of-the-art methodologies. Lastly, experiments performed verified that the proposed locomotion classification system is superior when compared to other conventional approaches, particularly when considering multimodal data. The novel multimodal IoT-based locomotion classification system has achieved an accuracy rate of 87.67% and 86.71% over the HWU-USP and Opportunity++ datasets, respectively. The mean accuracy rate of 87.0% is higher than the traditional methods proposed in the literature.

“…A deep learning framework will help facilities to cope with high costs and nursing shortages via ADL recognition [ 20 ]. Multiple hyper parameters can be used for each deep learning model to adjust the ADL recognition [ 21 ]. Therefore, we have proposed a unique framework for the ADL recognition of elderly people at smart homes and facilities using IoT-based multisensory devices.…”

Section: Introductionmentioning

confidence: 99%

Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework

Javeed,

Mudawi,

Alazeb

et al. 2023

Smart home monitoring systems via internet of things (IoT) are required for taking care of elders at home. They provide the flexibility of monitoring elders remotely for their families and caregivers. Activities of daily living are an efficient way to effectively monitor elderly people at home and patients at caregiving facilities. The monitoring of such actions depends largely on IoT-based devices, either wireless or installed at different places. This paper proposes an effective and robust layered architecture using multisensory devices to recognize the activities of daily living from anywhere. Multimodality refers to the sensory devices of multiple types working together to achieve the objective of remote monitoring. Therefore, the proposed multimodal-based approach includes IoT devices, such as wearable inertial sensors and videos recorded during daily routines, fused together. The data from these multi-sensors have to be processed through a pre-processing layer through different stages, such as data filtration, segmentation, landmark detection, and 2D stick model. In next layer called the features processing, we have extracted, fused, and optimized different features from multimodal sensors. The final layer, called classification, has been utilized to recognize the activities of daily living via a deep learning technique known as convolutional neural network. It is observed from the proposed IoT-based multimodal layered system’s results that an acceptable mean accuracy rate of 84.14% has been achieved.