Sport climbing brings together multiple parties i.e., climbers, climbing gym operators, route setters and climbing coaches, each with a specific set of goals. For example, climbers are interested in improving their climbing performance while climbing gym operators are constantly trying to improve their services and make optimal use of infrastructures. We asked a question, which IoT devices and software tools can be combined to provide effective solutions to problems faced in the everyday climbing practice. We think such systems can be designed to be both privacy-preserving and almost invisible to their users. In this talk we present an example of a climbing monitoring system that aims to provide statistics related to the frequency of ascents and -to some extent- climbing performance.

1. IoT in Climbing: Non-Invasive Activity Tracking
Marina Andrić
(based on a collaboration Unibz/Vertical-Life within the Salsa project)
Free University of Bozen-Bolzano
SFScon
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Marina Andric (unibz) SFScon 13 November 2020 2 / 12

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Some conferences associated with mobile sensing platforms
IEEE international conference on pervasive computing and communications (PerCom)
PerCom have evolved into highly active areas of research; they have found their way into many current
commercial systems due to the tremendous advances in a broad spectrum of technologies and topics including wireless
networking, mobile and distributed computing, sensor systems, ambient intelligence, and smart devices.
Conference on Ubiquitous Computing (UbiComp)
Ubicomp is an interdisciplinary field of research and development that utilizes and integrates pervasive, wireless,
embedded, wearable and/or mobile technologies to bridge the gaps between the digital and physical worlds.
International Symposium on Wearable Computers (ISWC)
ISWC is a premier interdisciplinary venue in which leading international researchers, designers, developers, and
practitioners in the field present and discuss novel results in all aspects of wearable computing This includes the design,
development, and deployment of wearable computing technologies and the understanding of human experiences and social
impacts that these technologies facilitate.
Marina Andric (unibz) SFScon 13 November 2020 3 / 12

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Tracking Sports Activities
• A. Möller, L. Roalter, S. Diewald, M. Kranz, N. Hammerla, P. Olivier, and T.
Plötz. (2012). GymSkill: A Personal Trainer for Physical Exercises. In
Proc. Int. Conf. Pervasive Computing and Communications (PerCom).
• R. D. Grober. (2009). An Accelerometer Based Instrumentation of the Golf
Club: Measurement and Signal Analysis. Arxiv
• S. Fothergill, R. Harle, and S. Holden. (2008). Modeling the model athlete:
Automatic coaching of Rowing technique. In Proc. Joint IAPR Int.
Workshop on Structural, Syntactic, and Statistical Pattern Recognition (SSPR
& SPR).
• A. Ahmadi, D. Rowlands, and D. A. James. (2010). Towards a wearable device
for skill assessment and skill acquisition of a Tennis player during the first
serve. Sports Technology, 2(3-4):129136, Feb.
Gymskill
Ergometer and rower augmented
with VICON markers
Marker placement with respect to
the Vicon Plug-In Gait modelMarina Andric (unibz) SFScon 13 November 2020 4 / 12

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The Sport of Climbing
Research could address problems concerning:
• Assessing climbing skill
• Climbing gym usage analytics
• Safety in climbing
• Climbing route recommendation
• Automatic climbing coaching
Marina Andric (unibz) SFScon 13 November 2020 5 / 12

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Climbing route
Quickdraw
Harness
Marina Andric (unibz) SFScon 13 November 2020 6 / 12

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Our approach
Smart quickdraw - a
quickdraw equipped with a
3-axial accelerometer.
Belayer
Climber
Attached
sensor
Data collection setup
Marina Andric (unibz) SFScon 13 November 2020 7 / 12

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Patterns in Sensor Signals
3-axial acceleration signal
Marina Andric (unibz) SFScon 13 November 2020 8 / 12

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Patterns in Sensor Signals
• Activities: falling
Fall
3-axial acceleration signal
Marina Andric (unibz) SFScon 13 November 2020 8 / 12

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Patterns in Sensor Signals
• Activities: falling, rope pulling
Rope pulling Rope pullingFall
3-axial acceleration signal
Marina Andric (unibz) SFScon 13 November 2020 8 / 12

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Patterns in Sensor Signals
• Activities: falling, rope pulling, lowering
Rope pulling Rope pullingFall
Lowering Lowering
3-axial acceleration signal
Marina Andric (unibz) SFScon 13 November 2020 8 / 12

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Patterns in Sensor Signals
• Activities: falling, rope pulling, lowering
Rope pulling Rope pullingFall
Lowering Lowering
3-axial acceleration signal
Marina Andric (unibz) SFScon 13 November 2020 8 / 12

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Free Software Frameworks
Marina Andric (unibz) SFScon 13 November 2020 9 / 12
Machine learning & data analysis

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Climbing Activity Recognition
Method Precision Recall
Random forest (n=100) 0.85 0.87
CatBoost 0.57 0.92
AdaBoost 0.70 0.93
Logistic regression 0.72 0.78
Dataset GT TP JI FP
Salewa 5 4 0.86 0
Vertikale1 4 4 0.92 0
Vertikale2 4 4 0.99 0
Vertikale3 4 4 0.95 0
Overall 17 16 0.93 0
Rope pulling activity recognition performance results.
GT : ground truth, TP : true positives, FP : false positives, JI : Jaccard index.
Marina Andric (unibz) SFScon 13 November 2020 10 / 12

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Climbing Performance Assessment
Comparison between climb duration and quickdraw movement magnitude for two climbers on
five routes (represented with different colors).
Marina Andric (unibz) SFScon 13 November 2020 11 / 12

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Climbing Performance Assessment
Comparison between climb duration and quickdraw movement magnitude for two climbers on
five routes (represented with different colors).
Marina Andric (unibz) SFScon 13 November 2020 11 / 12

17. Thank you
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SFScon