New Position Candidate Identification via Clustering toward an Extensible On-Body Smartphone Localization System
Abstract
:1. Introduction
- A data-driven method that finds an optimal parameter value for clustering is heuristically developed, which does not need to specify the number of clusters in advance, yet it uses a dataset to train the position classification component. An evaluation shows that the proposed approach is comparable to existing methods that specify the number of clusters in advance and that estimate the number of clusters on-the-fly. The re-trained classifier performs well with high accuracy, i.e., an accuracy of more than 0.94.
- A condition that determines the time of performing a new position candidate identification process is presented as a result of experiments varying the number of samples and the breakdown of the samples of new class candidates. The estimated minimum time to collect a sufficient number of samples is appropriate, i.e., 5–12 min. in three datasets used in the evaluation, enough to be implemented in daily use.
- By integrating the design principles that are presented in this work with those already known in our previous work, a complete picture of the incremental position addition framework during walking is presented.
2. Related Work
2.1. Usefulness of On-Body Smartphone Position Recognition
2.2. On-Body Device Position Recognition
2.3. Application of Machine Learning Techniques to Inertial Sensor Signals
3. Incremental Position Addition Framework
3.1. Overview
3.2. Our Previous Work and the Scope of This Article
4. Designing a New Position Candidate Identification Component
4.1. Finding an Optimal Value of eps
4.1.1. Parameters Used for Evaluating the Appropriateness of eps
4.1.2. Finding an Optimal for Hyperparameter Tuning
- Step 1:
- Perform DBSCAN on the training data of one person by giving each of the Z eps in a specific candidate of eps and calculate , , , and .
- Step 2:
- Exclude eps whose is more than the threshold value ().
- Step 3:
- Exclude eps whose is below the threshold ().
- Step 4:
- Exclude eps whose is above the threshold ().
- Step 5:
- Specify the eps with the maximum from the remaining set of eps as .
4.2. Experiment: Effectiveness of the Proposed Search Method
4.2.1. Searching Optimal
4.2.2. Evaluation on Clustering Performance Using Optimal
4.2.3. Datasets
4.2.4. Analysis of Searching Optimal eps
4.2.5. Results of the Clustering Performance
4.3. Discussion
5. Timing of New Position Candidate Identification
5.1. Overview
5.2. Experiment: Impact of the Number of Samples on the New Position Candidate Identification Process Performance
5.2.1. Method
5.2.2. Results
5.3. Experiment: Impact of the Breakdown of Positions on the New Position Candidate Identification Process Performance
5.3.1. Method
5.3.2. Results
5.4. Discussion
- The new position candidate identification process can be periodically invoked to check whether at least 33% of the data for each new class candidate relative to the number of original training data are stored. If the condition is satisfied, the result is adopted; otherwise, the samples remain stored in the novelty sample pool for future use.
6. Conclusions and Future Work
- When compared to a method that estimates the number of clusters (i.e., X-means), the proposed method performed clustering closer to the ideal number of positions.
- The proposed method showed a comparable level of FMI and Acc, as compared to a method that specifies the number of clusters in advance (i.e., k-means).
- The identification process can be invoked if at least 33% of the data for each new class candidate relative to the number of original training data are stored.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Literature | Position |
---|---|
Kunze et al. [16] | Wrist, head, trouser left pocket, and chest pocket |
Shi et al. [5] | Chest pocket, front/back trouser pocket, and hand |
Vahdatpour et al. [17] | Upper arm, forearm, waist, shin, thigh, and head |
Wiese et al. [18] | Pocket, bag, hand, and away from human |
Weenk et al. [19] | Pelvis, sternum, head, shoulder, upper arm, forearm, hand, upper/lower leg, and foot |
Shoaib et al. [20] | Trouser pocket, arm, wrist, and belt |
Coskun et al. [13] | Pocket, bag, and hand |
Alanezi et al. [14] | Jacket pocket, front/back trouser pocket, desk, and hand (calling, watching the screen in the portrait direction, and swinging during walking) |
Hoseinitabatabaei et al. [21] | Front trouser pocket, shoulder bag, hand, and belt |
Diaconita et al. [22] | Pocket, bag, hand, and desk (facing the ceiling and facing the surface of the desk) |
Sztyler et al. [15] | Head, chest, upper arm, waist, forearm, thigh, and shin |
Fujinami [23] | Neck (hanging), chest pocket, jacket pocket, front/back trouser pocket, and bag (backpack, hand bag, shoulder bag, and messenger bag) |
Yang et al. [24] | Jacket pocket, trouser pocket, bag, and hand |
Fujinami et al. [4] | Neck (hanging), chest pocket, jacket pocket, front/back trouser pocket, bag (backpack, hand bag, and shoulder bag), and hand (calling, watching the screen in the portrait direction, and swinging during walking) |
Shi et al. [25] | Chest pocket, front trouser pocket, jacket pocket, and hand |
Hasegawa et al. [26] | Bag, trouser pocket, cushion, towel, rubber, copper, wood, hand, and phone stand |
Bieshaar [6] | Jacket pocket, front/back trouser pocket, and backpack |
Sang et al. [27] | Arm, hand, and thigh |
Chen et al. [28] | Backpack, pocket, bag, and hand |
Guo et al. [29] | Backpack, flat, and hand (calling and swinging during walking) |
Li et al. [30] | Calf, thigh, upper/lower arm, and back |
Dataset | # Person | Position | Candidates (Pattern NF) | ||
---|---|---|---|---|---|
A [4] | 70 | 50 Hz | 256 | Neck (hanging), chest pocket, jacket pocket, front/back trouser pocket, bag (backpack, hand bag, and shoulder bag), and hand (calling, watching the screen in the portrait direction, and swinging during walking) | 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, and 7.5 |
B [23] | 20 | 25 Hz | 256 | Neck (hanging), chest pocket, jacket pocket, front/back trouser pocket, and bag (backpack, hand bag, shoulder bag, and messenger bag) | 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, and 7.5 |
C [20] | 10 | 50 Hz | 100 | Trouser pocket, arm, wrist, and belt | 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, 11.5, 12.0, and 12.5 |
DBSCAN (WC Pattern) | DBSCAN (NF Pattern) | DBSCAN () | X-Means | k-Means | |
---|---|---|---|---|---|
0.004 | 0.007 | 0.969 | - | - | |
+0.58 | +1.10 | - | +5.56 | - | |
0.932 | 0.935 | - | - | 0.954 | |
0.984 | 0.988 | - | - | 0.987 |
DBSCAN (WC Pattern) | DBSCAN (NF Pattern) | DBSCAN () | X-Means | k-Means | |
---|---|---|---|---|---|
0.002 | 0.003 | 0.998 | - | - | |
+0.34 | +0.54 | - | +6.54 | - | |
0.915 | 0.922 | - | - | 0.947 | |
0.927 | 0.940 | - | - | 0.969 |
DBSCAN (WC Pattern) | DBSCAN (NF Pattern) | DBSCAN () | X-Means | k-Means | |
---|---|---|---|---|---|
0.001 | 0.007 | 0.999 | - | - | |
- | +3.37 | - | |||
0.861 | 0.865 | - | - | 0.841 | |
0.975 | 0.977 | - | - | 0.997 |
Dataset A | Dataset B | Dataset C | |
---|---|---|---|
WC pattern | −0.10 | −0.20 | −0.07 |
NF pattern | −0.08 | −0.14 | 0.07 |
100% | 66% | 33% | 10% | |
---|---|---|---|---|
Dataset A | 0.99 | 0.98 | 0.94 | 0.35 |
Dataset B | 0.93 | 0.92 | 0.82 | 0.16 |
Dataset C | 0.98 | 0.98 | 0.97 | 0.65 |
100% | 66% | 33% | 10% | |
---|---|---|---|---|
Dataset A | +1.10 | +0.19 | ||
Dataset B | +0.54 | |||
Dataset C | +0.07 |
100% | 100% | 100% | 100% | 66% | 66% | 66% | 33% | 33% | 10% | |
---|---|---|---|---|---|---|---|---|---|---|
100% | 66% | 33% | 10% | 66% | 33% | 10% | 33% | 10% | 10% | |
Dataset A | 0.99 | 0.99 | 0.99 | 0.93 | 0.99 | 0.99 | 0.81 | 0.93 | 0.43 | 0.01 |
Dataset B | 0.98 | 0.97 | 0.96 | 0.86 | 0.96 | 0.95 | 0.81 | 0.91 | 0.23 | 0.01 |
Dataset C | 0.99 | 0.99 | 0.99 | 0.97 | 0.99 | 0.99 | 0.98 | 0.99 | 0.94 | 0.80 |
100% | 100% | 100% | 100% | 66% | 66% | 66% | 33% | 33% | 10% | |
---|---|---|---|---|---|---|---|---|---|---|
100% | 66% | 33% | 10% | 66% | 33% | 10% | 33% | 10% | 10% | |
Dataset A | +0.52 | +0.28 | +0.07 | |||||||
Dataset B | +0.39 | +0.31 | +0.13 | +0.09 | ||||||
Dataset C | +0.17 | +0.08 | +0.16 | +0.17 | +0.23 | +0.04 | +0.08 |
Dataset A | Dataset B | Dataset C | |
---|---|---|---|
The time per sample () | 5.12 s | 10.24 s | 2 s |
Number of persons (F) | 70 | 20 | 10 |
Number of positions (P) | 11 | 9 | 4 |
Number of all samples included in dataset () | 145,661 | 34,962 | 22,450 |
Sample per person per position (S) | approx. 189 | approx. 194 | approx. 561 |
Sample of 33% () | approx. 63 | approx. 65 | approx. 187 |
Time to satisfy the timing condition () | approx. 323 s | approx. 666 s | approx. 374 s |
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Saito, M.; Fujinami, K. New Position Candidate Identification via Clustering toward an Extensible On-Body Smartphone Localization System. Sensors 2021, 21, 1276. https://doi.org/10.3390/s21041276
Saito M, Fujinami K. New Position Candidate Identification via Clustering toward an Extensible On-Body Smartphone Localization System. Sensors. 2021; 21(4):1276. https://doi.org/10.3390/s21041276
Chicago/Turabian StyleSaito, Mitsuaki, and Kaori Fujinami. 2021. "New Position Candidate Identification via Clustering toward an Extensible On-Body Smartphone Localization System" Sensors 21, no. 4: 1276. https://doi.org/10.3390/s21041276