Laurence P. J. Kenney

Biomedical Engineering · 2009

A researcher in the Science corpus: 2 papers; trusts 13 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Biomedical Engineering — 27 citations into it; #5,191 of 10,734 by trust here, #5,019 by citations
• Computer Vision and Pattern Recognition — 25 citations into it; #14,177 of 21,575 by trust here, #10,334 by citations
• Physical Therapy, Sports Therapy and Rehabilitation — 15 citations into it; #287 of 390 by trust here, #147 by citations
• Artificial Intelligence — 10 citations into it
• Physiology — 9 citations into it
• Electrical and Electronic Engineering — 6 citations into it
• Signal Processing — 6 citations into it

Whom Laurence P. J. Kenney trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Tom Chau
2. Bijan Najafi
3. Miikka Ermes
4. Ling Bao
5. Tâm Huynh
6. Juha Pärkkä
7. Kamiar Aminian
8. Stéphane Mallat
9. Jani Mäntyjärvi
10. Bernt Schiele

Who trusts Laurence P. J. Kenney overall

1. Kerem Altun
2. Billur Barshan
3. Weijun Tao
4. D. V. D. Valk

Papers from Laurence P. J. Kenney's perspective

Ordered by how strongly the researchers Laurence P. J. Kenney trusts endorse each paper. Same corpus, a different order for every researcher.

1. Activity Recognition from User-Annotated Acceleration Data
2. Activity and Location Recognition Using Wearable Sensors (IEEE pervasive computing)
3. Neural Networks for Pattern Recognition
4. Multi-sensor Activity Context Detection for Wearable Computing (EUSAI)
5. Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement (Physiological Measurement)
6. Capturing human motion using body‐fixed sensors: outdoor measurement and clinical applications (Comput. Animat. Virtual Worlds)
7. A Hybrid Discriminative/Generative Approach for Modeling Human Activities (International Joint Conference on Artificial Intelligence)
8. Context is key (CACM)
9. A review of analytical techniques for gait data. Part 2: neural network and wavelet methods. (Gait & Posture)
10. A review of analytical techniques for gait data. Part 1: Fuzzy, statistical and fractal methods. (Gait & Posture)

Their papers

• Activity identification using body-mounted sensors—a review of classification techniques (2009) — 617 citations
• A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data (2009) — 570 citations