ADL Recognition Through Machine Learning Algorithms on IoT Air Quality Sensor Dataset

Gambi, Ennio; Temperini, Giulia; Galassi, Rossana; Senigagliesi, Linda; Santis, Adelmo De

doi:10.1109/jsen.2020.3005642

Cited by 25 publications

(12 citation statements)

References 17 publications

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“…In an ST-based pulse radar, (5) becomes where n steps is the number of steps in the threshold sweep that causes an SNR loss and G ST is the SNR gain obtained from multiple threshold levels covering the noise region around the signal value, which results in an integration effect. The largest signal in the sampled frame can be used to determine the threshold sweep range, which occurs at the minimum distance, in addition to the noise.…”

Section: Data Acquisition From Uwb Radarmentioning

confidence: 99%

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Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning

Noori¹,

Uddin

Tørresen

2021

IEEE Access

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With recent advances in the field of sensing, it has become possible to build better assistive technologies. This enables the strengthening of eldercare with regard to daily routines and the provision of personalised care to users. For instance, it is possible to detect a person's behaviour based on wearable or ambient sensors; however, it is difficult for users to wear devices 24/7, as they would have to be recharged regularly because of their energy consumption. Similarly, although cameras have been widely used as ambient sensors, they carry the risk of breaching users' privacy. This paper presents a novel sensing approach based on deep learning for human activity recognition using a non-wearable ultra-wideband (UWB) radar sensor. UWB sensors protect privacy better than RGB cameras because they do not collect visual data. In this study, UWB sensors were mounted on a mobile robot to monitor and observe subjects from a specific distance (namely, 1.5-2.0 m). Initially, data were collected in a lab environment for five different human activities. Subsequently, the data were used to train a model using the state-of-the-art deep learning approach, namely long short-term memory (LSTM). Conventional training approaches were also tested to validate the superiority of LSTM. As a UWB sensor collects many data points in a single frame, enhanced discriminant analysis was used to reduce the dimensions of the features through application of principal component analysis to the raw dataset, followed by linear discriminant analysis. The enhanced discriminant features were fed into the LSTMs. Finally, the trained model was tested using new inputs. The proposed LSTM-based activity recognition approach performed better than conventional approaches, with an accuracy of 99.6%. We applied 5-fold cross-validation to test our approach. We also validated our approach on publically available dataset. The proposed method can be applied in many prominent fields, including human-robot interaction for various practical applications, such as mobile robots for eldercare.

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Section: Data Acquisition From Uwb Radarmentioning

confidence: 99%

“…Understanding the current state and context of users is crucial for assisting in their everyday lives. Human movement has been actively studied using distinguished ambient sensors [4], [5]. Previously, video-based sensors have been used for human activity recognition and fall detection [6].…”

Section: Introductionmentioning

confidence: 99%

Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning

Noori¹,

Uddin

Tørresen

2021

IEEE Access

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“…With respect to the integration of action recognition solutions in ARDs, different studies revealed that this capability is fundamental for the interactions with the users [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. Most of the approaches need a set of sensors to be installed in the home of the user, e.g., [ 30 , 31 , 32 ]. One can also find works where the activity recognition is performed with the use of body sensors, e.g., [ 33 , 34 ].…”

Section: Related Workmentioning

confidence: 99%

A Low-Cost Assistive Robot for Children with Neurodevelopmental Disorders to Aid in Daily Living Activities

López-Sastre

Baptista-Ríos

Acevedo-Rodríguez

et al. 2021

IJERPH

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In this paper, we present a new low-cost robotic platform that has been explicitly developed to increase children with neurodevelopmental disorders’ involvement in the environment during everyday living activities. In order to support the children and youth with both the sequencing and learning of everyday living tasks, our robotic platform incorporates a sophisticated online action detection module that is capable of monitoring the acts performed by users. We explain all the technical details that allow many applications to be introduced to support individuals with functional diversity. We present this work as a proof of concept, which will enable an assessment of the impact that the developed technology may have on the collective of children and youth with neurodevelopmental disorders in the near future.

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“…The human skeleton is closely related to the nearby space while the individual is moving. The computation of the angles between the limbs and the horizontal axis is the same as for the joint angles, by plugging the vectors into Equation (8). There are a total of four angles shown in Figure 3.…”

Section: Angle Between the Limb End And The Horizontalmentioning

confidence: 99%

“…Motion recognition methods that focus on the description of image features have inspired researchers to improve the efficiency of the technology. ADL recognition for years has been discussed for years and some work has been carried out to improve sensor ability [8]. Recent studies have mostly concentrated on the development of methodologies, including machine learning, probabilistic finite, entropy-based, and knowledge-driven approaches [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition

Hayati

Tseng

et al. 2020

Applied Sciences

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Health care for independently living elders is more important than ever. Automatic recognition of their Activities of Daily Living (ADL) is the first step to solving the health care issues faced by seniors in an efficient way. The paper describes a Deep Neural Network (DNN)-based recognition system aimed at facilitating smart care, which combines ADL recognition, image/video processing, movement calculation, and DNN. An algorithm is developed for processing skeletal data, filtering noise, and pattern recognition for identification of the 10 most common ADL including standing, bending, squatting, sitting, eating, hand holding, hand raising, sitting plus drinking, standing plus drinking, and falling. The evaluation results show that this DNN-based system is suitable method for dealing with ADL recognition with an accuracy rate of over 95%. The findings support the feasibility of this system that is efficient enough for both practical and academic applications.

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ADL Recognition Through Machine Learning Algorithms on IoT Air Quality Sensor Dataset

Cited by 25 publications

References 17 publications

Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning

Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning

A Low-Cost Assistive Robot for Children with Neurodevelopmental Disorders to Aid in Daily Living Activities

Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition

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