Regular exercise is one of the most important factors in maintaining a good state of health. In the past, different systems have been proposed to assist people when exercising. While most of those systems focus only on cardio exercises such as running and cycling, we exploit smartphones to support leisure activities with a focus on resistance training. We describe how off-the-shelf smartphones without additional external sensors can be leveraged to capture resistance training data and to give reliable training feedback. We introduce a dynamic time warping-based algorithm to detect individual resistance training repetitions from the smartphone’s acceleration stream. We evaluate the algorithm in terms of the number of correctly recognized repetitions. Additionally, for providing feedback about the quality of repetitions, we use the duration of an individual repetition and analyze how accurately start and end times of repetitions can be detected by our algorithm. Our evaluations are based on 3,598 repetitions performed by ten volunteers exercising in two distinct scenarios, a gym and a natural environment. The results show an overall repetition miscount rate of about 1 % and overall temporal detection error of about 11 % of individual repetition duration

1. Exercise repetition detection
for resistance training based
on smartphones,
+ Pers. Ubiquit. Comput.
(2013),
- Igor Pernek et al,
/ 맹욱재
x 2015 Rainy Fall

2. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 2
Contents
1. Intro
2. Related Works
3. System & Algorithm
4. Evaluation

3. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 3
Intro - Why this paper?
WookFit : Wearable Device for Breast Cancer Survivors doing Resistance ExcersiceElastic-band stretching Counter

4. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 4
Intro - Why this paper?(Cont.)
Key word for searching paper
Breast Cancer Rehabilitations
Resistance Excersise(traning)
Home-based Excersise(traning)
Lymphedema
Theraband(Resistance band)
Wearable
IoT
Quality of Life
Smartphone Application
Accelerometer

5. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 5
Key word for searching paper
Breast Cancer Rehabilitations
Resistance Excersise(traning)
Home-based Excersise(traning)
Lymphedema
Theraband(Resistance band)
Wearable
IoT
Quality of Life
Smartphone Application
Accelerometer
Intro - Why this paper?(Cont.)

6. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 6
Intro - Take away
1. Dynamic Time Warping algorithm can accurately count
the number of repetition, duration.
2. Giving real time feedback on Quality of Performance
can be done using duration, start, end time of repetition

7. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 7
Intro - Abstract
Problem
Solution
Most of systems focus only on cardio exercises such as running and cycling
Exploit smartphones to support leisure activities with a focus on resistance training.
Off-the-shelf smartphones without additional external sensors can be leveraged
to capture resistance training data and to give reliable training feedback
Algorithm
Dynamic Time Warping based algorithm to detect individual resistance training
repetitions from the smartphone’s acceleration stream

8. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 8
Intro - Abstract
Feedback in detail
Providing feedback about the quality of repetitions, use the duration of an
individual repetition and analyze how accurately start and end times of repetitions
can be detected by our algorithm
In terms of the number of correctly recognized repetitions.
3,598 repetitions performed by 10 volunteers exercising in 2 distinct scenarios
a gym and a natural environment.
The results show an overall repetition miscount rate of about 1 % overall
temporal detection error of about 11 % of individual repetition duration.
Evalution

9. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 9
Related Works
Manual input problem
manual input is cumbersome, taking notes about resistance training is often avoided,
negatively affect people’s activity levels and decrease their motivation for regular training
https://www.jefit.com/products/androidpro/index.php
https://play.google.com/store/apps/details?id=com.namuan.health&hl=ko

10. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 10
Related Works - Fitlinxx
https://www.youtube.com/watch?time_continue=303&v=BfnPogUokKs
1. very expensive, as the gym has to be equipped with specific exercise machines and
infrastructure, and has therefore not gained broader popularity
2. have to log in to the system prior to performing any exercise,

11. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 11
Related Works - Recognizing Upper Body Postures using Textile Strain Sensors

12. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 12
Related Works- The Virtual Trainer: Supervising Movements Through a Wearable Wireless Sensor Network
the algorithm relies on exercise specific
features, such as the position of the elbow
during the biceps curl exercise, and can
therefore not be easily applied to a general
resistance training exercise.

13. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 13
Related Works- Implementation and evaluation of the personal wellness coach
The acceleration stream-laptop computer
threshold-based low-pass filtering algorithm:
each transition from resting to exercise state to
count the number of repetitions. the number of
time units in each state is analyzed to detect
struggling during exercise execution. all the
repetitions have a similar single peak acceleration
footprint, which is not true for exercises
performed in different environments

14. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 14
Related Works -Tracking Free-Weight Exercises
feature phone + 2 external accelerometer sensors.
data from a glove & chest belt, only derive info
for a limited set of free-weight exercises.
simple peak detection algorithms that are
no detect a repetition’s start and end time
->lack capabilities for providing qualitative
feedback based on the duration of repetitions.

15. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 15
Related Works - myHealthAssistant: A Phone-based Body Sensor
Network that Captures the Wearer’s Exercises throughout the Day
advance ‘Tracking Free-Weight Exercises' by
making the repetition detection algorithm robust
to different exercising speeds

16. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 16
Related Works- What can an arm holster worn smart phone do for activity recognition?
arm worn smartphones to
recognize a number of upper
body resistance training
exercises from a continuous
acceleration stream. Main
focus lies on spotting series
of exercises, repetitions-
peak detection. start & end
times of individual
repetitions cannot be
detected.

17. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 17
Related Works
Missing information
1) not ubiquitous -> mobile(smartphone)
2) targeted to a set of specific exercises
-> broader set of exercises, performed using different types of equipment
3) not support fine-grained tracking of individual repetitions.
important to give feedback about the correctness and quality of exercising
-> by detection of duration of individual repetitions.
WookFit
1) mobile(smartphone) + Wearable device
2) targeted to a set of specific exercises
3) not support fine-grained tracking of individual repetitions.

18. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 18
System - Requirement
1) Should not add high extra cost nor demand the cumbersome installation of
additional sensors and tools
=> off-the-shelf smartphones
2) Easy to use interface, be encouraging by suggesting particular exercises, and give
feedback on the exercising performance as well as the training progress
=> Quality of Performance Feedback
3) Usable in different environments including gyms and natural environments and
cover a wide range of exercises
=> Constrained(gym, fitness center) + Unconstrained(free-weight, resistance band,
body weight)

19. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 19
System - Resistance Training Assistant

20. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 20
System - Resistance Training Assistant
1) Download different training plans and exercises from an online training database.
2) The application proposes exercises to perform along with some additional information -
the number of repetitions required or the intensity(weight)
3) Realtime feedback about the desired exercise effect by evaluating the duration of the
repetitions.
4) Permanently stores the number and the intensity of repetitions for each exercise and
allows users to upload it to an online profile to track progress and observe exercising
trends (information may be shared within an online community).

21. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 21
Unconstrained Exercise Constrained Exercise
System - Device Position

22. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 22
System - Selected Exercise

23. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 23
Algorithm - Overview

24. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 24
Algorithm - Dynamic Time Warping
Definition
In time series analysis, algorithm for measuring similarity between two temporal
sequences which may vary in time or speed

25. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 25
Evaluation - Precision, Recall, F score

26. UX Lab, Graduate School of Convergence Science and Technology, Seoul National Univ. 26
Evaluation - Box plot for Temporal error of s,e
Fig. 5 Temporal error of detecting individual repetition start and end times normalized by the total duration of
the repetition for externally generated repetition patterns (median, quartiles, and extreme values)

27. 감사합니다
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