Estimation of Violin Bow Pressure Using Photo-Reflective Sensors presents a method for quantitatively estimating bow pressure during violin playing using photo-reflective sensors attached to the bow. Five sensors measure the distance between the bow stick and hair, which changes with applied pressure. A random forest regression model is trained on sensor distance values and actual pressure measurements to estimate pressure based solely on sensor values. In experiments, the model estimated bow pressure with an R2 of 0.84, MAE of 0.11N, and MAPE of 19.1% when tested on data from an experienced violinist. The goal is to provide visual feedback to support practice by quantifying bow pressure.

1. Estimation of Violin Bow Pressure
Using Photo-Reflective Sensors
Yurina Mizuho, Riku Kitamura, and Yuta Sugiura
Keio University
01 Introduction
• The violin is hard to learn
• Most performers simply adjust their
playing motions following their
senses and experience
Background
• Bowing has a strong influence on the
tone (bow speed, position, pressure)
• Related methods using strain gauges
are difficult to install on existing
instruments
Motivation
• Using photo-reflective sensors
• Quantitatively estimate and visualize
bow pressure to support practice
Approach
04 Application
• Real-time bow pressure estimation
• Visual feedback during violin playing
• Instructors: quantitatively grasp students’ bow pressure
• Amateur performers: improve their playing by comparing
the bow pressure with past results and that of professionals
• Examine the effect of bow pressure feedback on practice
• Consider other feedback methods (tactile, auditory)
• Improve the accuracy of bow pressure estimation by
tracking the bow position where the pressure is applied
• The same model cannot be used with different bow tensions
International Conference on Multimodal Interaction (ICMI) 2023 Mail: ymizuho@keio.jp
Measuring Principle
• The distance between the bow stick and the hair
changes according to the bow pressure on the string
• Five photo-reflective sensors are attached to a bow
stick to measure the sensor values as the distance
changes
Estimation Principle
1. Obtain the sensor values and the actual
bow pressure value simultaneously
2. Train a random forest regression model
3. Estimate bow pressure based only on the
sensors' distance values using the model
03 Experiments
𝑦𝑦 = 𝑓𝑓(𝑥𝑥)
Bow pressure Sensor values (Distance)
Regression model
05 Discussion
02 Method
Participant
• One experienced violinist
Measurement
• The bow is slid on a load cell
• About 16.7 s×60 fps
=1000 frame
Dataset
• Sampled 150 frames without
distribution bias
• ×30 measurements
⇒4500 frames
• 30-fold cross validation
Results
• R2: 0.84
• MAE: 0.11 N
• MAPE: 19.1%