A Deep Learning Approach for Human Activities Recognition From Multimodal Sensing Devices

Ihianle, Isibor Kennedy; Nwajana, Augustine O.; Ebenuwa, Solomon H.; Otuka, Richard I.; Owa, Kayode; Orisatoki, Mobolaji Olawumi

doi:10.1109/access.2020.3027979

Cited by 62 publications

(31 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Accuracy of the proposed model has the highest accuracy of 98.67%. In the second place, Spatio-Temporal Deep Learning [ 46 ] has accuracy of 98.53%, in third-place Deep learning low power device [ 41 ] has accuracy of 98.2% while in the third-place, CNN + BLSTM [ 44 ] has accuracy of 97.8%. Based on Precision, the proposed model has achieved the highest precision of 98.66%.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

“…Based on Precision, the proposed model has achieved the highest precision of 98.66%. In the second place, Random Forest Classifier [ 43 ] has precision of 98.1% while in the third-place CNN + BLSTM [ 44 ] has precision of 97.8%. Based on recall, the proposed model has achieved the highest recall of 98.67%.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

“…On basis F-measure, the proposed model has achieved the highest F-measure with 0.987. In the second the place, the Random Forest Classifier [ 43 ] has 0.981of F-measure while in the third place, CNN + BLSTM [ 44 ] has 0.978 of F-measure. Generally, the proposed model has performed the best across the four performance evaluation criteria.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent system for human activity recognition in IoT environment

Khaled

Abu-Elnasr

Elmougy

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

In recent years, the adoption of machine learning has grown steadily in different fields affecting the day-to-day decisions of individuals. This paper presents an intelligent system for recognizing human’s daily activities in a complex IoT environment. An enhanced model of capsule neural network called 1D-HARCapsNe is proposed. This proposed model consists of convolution layer, primary capsule layer, activity capsules flat layer and output layer. It is validated using WISDM dataset collected via smart devices and normalized using the random-SMOTE algorithm to handle the imbalanced behavior of the dataset. The experimental results indicate the potential and strengths of the proposed 1D-HARCapsNet that achieved enhanced performance with an accuracy of 98.67%, precision of 98.66%, recall of 98.67%, and F1-measure of 0.987 which shows major performance enhancement compared to the Conventional CapsNet (accuracy 90.11%, precision 91.88%, recall 89.94%, and F1-measure 0.93).

show abstract

Section: Evaluation and Resultsmentioning

confidence: 99%

Section: Evaluation and Resultsmentioning

confidence: 99%

Section: Evaluation and Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent system for human activity recognition in IoT environment

Khaled

Abu-Elnasr

Elmougy

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…Multiple authors have developed models that use a series of CNN layer first to fuse sensor data from multiple modalities before passing it to a LSTM network [33][34][35][36][37]. These achieve only minor improvements in performance classification with 95-96% accuracies.…”

Section: Related Workmentioning

confidence: 99%

Understanding LSTM Network Behaviour of IMU-Based Locomotion Mode Recognition for Applications in Prostheses and Wearables

Sherratt

Plummer

Iravani

2021

Sensors

View full text Add to dashboard Cite

Human Locomotion Mode Recognition (LMR) has the potential to be used as a control mechanism for lower-limb active prostheses. Active prostheses can assist and restore a more natural gait for amputees, but as a medical device it must minimize user risks, such as falls and trips. As such, any control system must have high accuracy and robustness, with a detailed understanding of its internal operation. Long Short-Term Memory (LSTM) machine-learning networks can perform LMR with high accuracy levels. However, the internal behavior during classification is unknown, and they struggle to generalize when presented with novel users. The target problem addressed in this paper is understanding the LSTM classification behavior for LMR. A dataset of six locomotive activities (walking, stopped, stairs and ramps) from 22 non-amputee subjects is collected, capturing both steady-state and transitions between activities in natural environments. Non-amputees are used as a substitute for amputees to provide a larger dataset. The dataset is used to analyze the internal behavior of a reduced complexity LSTM network. This analysis identifies that the model primarily classifies activity type based on data around early stance. Evaluation of generalization for unseen subjects reveals low sensitivity to hyper-parameters and over-fitting to individuals’ gait traits. Investigating the differences between individual subjects showed that gait variations between users primarily occur in early stance, potentially explaining the poor generalization. Adjustment of hyper-parameters alone could not solve this, demonstrating the need for individual personalization of models. The main achievements of the paper are (i) the better understanding of LSTM for LMR, (ii) demonstration of its low sensitivity to learning hyper-parameters when evaluating novel user generalization, and (iii) demonstration of the need for personalization of ML models to achieve acceptable accuracy.

show abstract

“…In the last years, several research teams worked on mobility related activity (MA) recognition such as walking, sitting, standing, running, etc. [22], [23]. With the help of classical machine learning or deep learning algorithms they reached impressive recognition rate for those MAs (sometimes even over 99%).…”

Section: Related Workmentioning

confidence: 99%

Acti-DM1: Monitoring the Activity Level of People With Myotonic Dystrophy Type 1 Through Activity and Exercise Recognition

et al. 2021

View full text Add to dashboard Cite

Myotonic dystrophy type 1 (DM1) is a rare disease where the highest prevalence is found in the small geographical region of Saguenay-Lac-St-Jean in Quebec, Canada. This disease impacts the quality of life and the ability of the affected people to pursue their normal day to day activities. To attenuate the impact of DM1, one could suggest that physiotherapists or other health professionals prescribe adapted physical exercise programs to carry out at home. It is, however, unpractical for the professionals to monitor every patient during their program and prohibitive in terms of cost. One alternative solution is to rely on the use of ambient technologies. To do so, in this research, our team developed an assistive system able to recognize simple mobility related activities (MA) and monitor each exercise performed during training sessions. The system can help the person by providing guidance and motivation throughout the training. The system was tested on 10 persons affected by the disease in their home for 10 weeks. The results obtained are encouraging and we discuss them in comparison with our previous work conducted in lab settings. Finally, to help further advances the research, the datasets are openly available online to the community.

show abstract

A Deep Learning Approach for Human Activities Recognition From Multimodal Sensing Devices

Cited by 62 publications

References 31 publications

Intelligent system for human activity recognition in IoT environment

Intelligent system for human activity recognition in IoT environment

Understanding LSTM Network Behaviour of IMU-Based Locomotion Mode Recognition for Applications in Prostheses and Wearables

Acti-DM1: Monitoring the Activity Level of People With Myotonic Dystrophy Type 1 Through Activity and Exercise Recognition

Contact Info

Product

Resources

About