Gesture recognition based on 3D accelerometer for cell phones interaction

He, Zhenyu; Jin, Lianwen; Zhen, Li-Xin; Huang, Jiancheng

doi:10.1109/apccas.2008.4745999

Cited by 13 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Algorithms for falling detection of these persons could be developed ( [7], or ideas from [8]). In this way, the usability of this system increases.…”

Section: ) Movement and Behavior Analysismentioning

confidence: 99%

Intelligent features in TELEASIS telecare system

Stoicu-Tivadar

Berian

2011

2011 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI)

View full text Add to dashboard Cite

The paper describes several original solutions to increase the in-built "intelligence" of a telecare system increasing the usability of the telecare system, reducing the burden of the monitoring and medical personnel and increasing the quality of care is the main beneficiary. One solution is using a warning component implemented at the server-and locally. It is presented the need of alarms (as logical combinations of threshold levels for the values read from the sensors at the local level), the approach to define and implement these in the server, and the communication of the alarm scenarios to the local computers using Web Services. Further ways to improve the "intelligent" functionalities of the system are briefly analyzed: intelligent processing of medical signals acquired (ECGs), movement and behavior analysis, context-based communication facilities, decision support, data mining functions at central level.

show abstract

“…Algorithms for falling detection of these persons could be developed ( [7], or ideas from [8]). In this way, the usability of this system increases.…”

Section: ) Movement and Behavior Analysismentioning

confidence: 99%

Intelligent features in TELEASIS telecare system

Stoicu-Tivadar

Berian

2011

2011 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI)

View full text Add to dashboard Cite

show abstract

“…It is well established [32,33] that the extraction of frequency domain features (i.e., the power characteristics of the signal) improves performance. These are highly relevant in the classification of repetitive motion based on accelerometer data, as FFT detects periodic signals and splits them into their harmonic components, reducing the dimensionality of data.…”

Section: Data Preprocessing Feature Extraction and Trainingmentioning

confidence: 99%

Leveraging Edge Computing ML Model Implementation and IoT Paradigm towards Reliable Postoperative Rehabilitation Monitoring

Faliagka,

Skarmintzos,

Panagiotou

et al. 2023

Electronics

View full text Add to dashboard Cite

In this work, an IoT system with edge computing capability is proposed, facilitating the postoperative surveillance of patients who have undergone knee surgery. The main objective is to reliably identify whether a set of orthopedic rehabilitation exercises is executed correctly, which is critical since it is often necessary to supervise patients during the rehabilitation period so as to avoid injuries or long recovery periods. The proposed system leverages the Internet of Things (IoT) paradigm in combination with deep learning and edge computing to classify the extension–flexion movement of one’s knee via embedded machine learning (ML) classification algorithms. The contribution of the proposed work is multilayered, as this paper proposes a system tackling the challenges at the embedded system level, algorithmic level, and user-friendliness level considering a performance evaluation, including the metrics at the power consumption level, delay level, and throughput requirement level, as well as its accuracy and reliability. Furthermore, as an outcome of this work, a dataset of labeled knee movements is freely available to the research community with no limitations. It also provides real-time movement detection with an accuracy reaching 100%, which is achieved with an ML model trained to fit a low-cost off-the-shelf Bluetooth Low Energy platform. The proposed edge computing approach allows predictions to be performed on device rather than solely relying on a Cloud service. This yields critical benefits in terms of wireless bandwidth and power conservation, drastically enhancing device autonomy while delivering reduced event detection latency. In particular, the “on device” implementation is able to yield a drastic 99.9% wireless data transfer reduction, a critical 39% prediction delay reduction, and a valuable 17% increase in the event prediction rate considering a reference period of 60 s. Finally, enhanced privacy comprises another significant benefit from the implemented edge computing ML model, as sensitive data can be processed on site and only events or predictions are shared with medical personnel.

show abstract

“…Song et al used an SVM for hand shape classification that was combined with a particle filtering estimation framework for 3D body postures and an LDCRF for overall recognition [84]. Other 3D gesture recognizers that utilize SVMs include [80,[95][96][97][98][99][100][101].…”

Section: Hidden Markov Models Although Hidden Markovmentioning

confidence: 99%

“…Recognition approach Number of gestures Accuracy Pang and Ding [58] HMMs with kinematic features 12 91.2% Wan et al [60] HMMs with sparse coding 4 94.2% Lee and Cho [61] Hierarchical HMMs 3 Approx. 80.0% Whitehead and Fox [68] Standard HMMs 7 91.4% Nguyen et al [66] Two-stage HMMs 10 95.3% Chen et al [63] HMMs with Fourier descriptors 20 93.5% Pylvänäinen [67] HMMs without rotation data 10 99.76% Chung and Yang [71] Threshold CRF 12 91.9% Yang et al [72] Two-layer CRF 48 93.5% Yang and Lee [73] HCRF with BoostMap embedding 24 87.3% Song et al [78] HCRF with temporal smoothing 10 93.7% Liu and Jia [80] HCRF with manifold learning 10 97.8% Elmezain and Al-Hamadi [83] LDCRF with depth camera 36 96.1% Song et al [84] LDCRF with filtering framework 24 75.4% Zhang et al [85] Fuzzy LDCRF 5 91.8% Huang et al [88] SVM with Gabor filters 11 95.2% Hsieh et al [89] SVM with Fourier descriptors 5 93.4% Hsieh and Liou [90] SVM with Haar features 4 95.6% Dardas and Georganas [92] SVM with bag of words 10 96.2% Chen and Tseng [93] Fusing multiple SVMs 3 93.3% Rashid et al [94] Combining SVM with HMM 18 98.0% Yun and Peng [101] Hu moments with SVM 3 96.2% Ren and Zhang [99] SVM with min enclosing ball 10 92.9% Wu et al [100] Frame-based descriptor with SVM 12 95.2% He et al [96] SVM with Wavelet and FFT 17 87.4% Nisar et al [104] Decision trees 26 95.0% Jeon et al [105] Multivariate fuzzy decision trees 10 90.6% Zhang et al [106] Decision trees fused with HMMs 72 96.3% Fang et al [107] Hierarchical Decision trees 14 91.6% Miranda et al [109] Decision forest with key pose learning 10 91.5% Keskin et al [41] Decision forest with SVM 10 99.9% Keskin et al [110] Shape classification forest 24 97.8% Negin et al…”

Section: Authormentioning

confidence: 99%

3D Gestural Interaction: The State of the Field

LaViola

2013

ISRN Artificial Intelligence

View full text Add to dashboard Cite

3D gestural interaction provides a powerful and natural way to interact with computers using the hands and body for a variety of different applications including video games, training and simulation, and medicine. However, accurately recognizing 3D gestures so that they can be reliably used in these applications poses many different research challenges. In this paper, we examine the state of the field of 3D gestural interfaces by presenting the latest strategies on how to collect the raw 3D gesture data from the user and how to accurately analyze this raw data to correctly recognize 3D gestures users perform. In addition, we examine the latest in 3D gesture recognition performance in terms of accuracy and gesture set size and discuss how different applications are making use of 3D gestural interaction. Finally, we present ideas for future research in this thriving and active research area.

show abstract

Gesture recognition based on 3D accelerometer for cell phones interaction

Cited by 13 publications

References 7 publications

Intelligent features in TELEASIS telecare system

Intelligent features in TELEASIS telecare system

Leveraging Edge Computing ML Model Implementation and IoT Paradigm towards Reliable Postoperative Rehabilitation Monitoring

3D Gestural Interaction: The State of the Field

Contact Info

Product

Resources

About