Comparison with Accelerometer Based Approaches

The proposed approach achieves an average classification rate of 87.28% in the worst case (82.72%∼90.42%). In order to provide a horizontal comparison with existing accelerometer based motion classification approaches, we browsed the mostly cited works and listed them in Table 4.10.

Table 4.10: Comparison between accelerometer based approaches and the proposed approach.

Literature Sensor Classifier Candidate Motion Number Accuracy Range(%) J. Kwapisz[KWM10] Single Accl. Logistic Regression 5 61.5 ∼ 98.3

S. Zhang[ZMNZ10] Single Accl. SVM 4 49.1 ∼ 87.2

J. Mantyjarvi[MHS01] Multiple Accl. MLP Neural Network 4 83.0 ∼ 90.0 X. Long[LYA09] Single Accl. Principle Component Analysis 6 71.3 ∼ 92.9

Our Approach RF SVM 7 82.7 ∼ 90.4

Considering classification accuracy, existing accelerometer based approaches as high as 98.3% for specific candidate motions. However, both Kwapisz’s and Zhang’s work suffers from low accuracy when classifying static cases such as standing and sitting. The proposed approach in this chapter may not score over 98% classification rate, but its accuracy range is relatively small, resulting in more stable performance. Apart from that, most of the accelerometer based approaches are capable to classify standing, sitting, walking and running. Kwapisz’s and Long’s work also covered on the stair cases. The proposed approach supports a broader candidate motion set, which is suitable for first responder applications. Last but not the least, it worth mentioning that in general classification problems, additional information usually improves the classification accuracy as long as it’s tightly coupled with the problem itself. The hybrid of accelerometer based approach and RF based approach may further improve the performance.

4.6

Summary.

In this chapter, we investigated the feasibility of classifying different human mo- tions based on RF features collected by on-body health monitoring system. Em- pirical data of 3 human subjects performing seven different candidate motions were obtained by VNA based measurement system. A set of 10 most significant RF fea- tures are derived and their pair wise correlation were examined. All-data-at-once

SVM was trained utilizing extracted RF features to classify the candidate motions. Classification accuracy based on entire 10 features was found to be around 88.69%. To thoroughly understand the performance of our approach, effect of both hu- man motion and sensor location has been investigated. We divided the motion set into static and dynamic categories and showed how these two aspects influence the detailed classification accuracy. While the results shown in this study are promising, we also analyzed the potential of decreasing necessary feature number to simplify the implementation of overall approach. With 8 most important RF features, the proposed scheme still achieved over 80% classification rate for all candidate motions. That number may be further reduced if less candidate motions are considered.

Our proposed scheme was fully capable for accurate human motion classification on a real-time, continuous basis. Such approach can be regarded as the first step toward realizing motion classification functionality on on-body monitoring networks. With the presented investigation results, it could be shown that the proposed scheme can support applications such as first responder survival system or smart healthcare system. Since SVM is only one of the available supervised learning algorithm in the open literature, the future work may include the implementation and comparison of multiple algorithms such as K-nearest algorithm, Neural network algorithm and etc.

Chapter 5

In-Body Radio Propagation and

Wireless Capsule Endoscopy

Hybrid Localization.

Wireless Capsule Endoscopy (WCE) is progressively emerging as the most popular non-invasive imaging tool for the diagnostic of Gastrointestinal (GI) tract diseases such as inflammatory bowel disease, ulcerative colitis and colorectal cancer. The WCE is a swallowable, pill-like micro-robot equipped with a tiny camera and a LED

illuminating system for capturing images of the inside of GI tract [IMGS00][KZS+_12][KB11].

A Radio Frequency (RF) transmission module is embedded in the WCE for sending the captured images wirelessly to the external on-body receivers [CMD11]. Theo- retically, the transmitted RF signals and captured images should allow us to localize the capsule and reconstruct its 3D movement path inside the human small intes- tine. Precise 3D reconstruction of the movement path helps physicians to associate the absolute location information with the intestinal abnormality upon observation. Apart from that, the path reconstruction also enables other micro-robotic surgeries

and at the same time unfolds the mysterious interior small intestine environment to the research community for educational purposes. Due to the complex and non- homogeneous environment of inside human body, fourteen years after the invention of WCE [IMGS00], 3D reconstruction of the WCE movement path inside the small intestine is still in its infancy.

The key to implement 3D reconstruction is the precision of localization inside the small intestine, an organ with an average length of seven meters randomly curled

and concentrated in the several hundreds cm3 of cramped space inside human ab-

domen [TAZL12][PBY+12]. To meet the demand on WCE location information,

many attempts have been made during the past few years to localize the WCE, and thus, reconstruct the 3D path. Existing literatures investigated various WCE localization approaches including radiological imaging, magnetic localization, image processing, RF localization, and even inertial sensing [KK15][TAZL12], but these location estimations are crude and the accuracy does not exceed a few centime-

ters [PBY+_{12][FC08][KPM11]. Without adequate localization accuracy, any kind of}

3D reconstruction loses its fidelity. To further enhance the WCE localization per- formance and enable 3D reconstruction of the path, a very intuitive solution is to develop hybrid localization systems that benefit from the combination of multiple independent measurements of capsule location [PBM13][PFJ12].

In our most recent research we proposed a prototype WCE hybrid localization approach that combines the received signal strength (RSS) based RF localization with the image processing based movement tracking [BPM15]. In this chapter, we investigate the problem from the analytical perspective and derive the 3D Posterior Cramer-Rao Lower Bound (PCRLB) for the WCE hybrid localization. The PCRLB serves as the fundamental framework for performance analysis of the hybrid local- ization approach and it can be applied to RF localization with both RSS ranging

and Time-of-Arrival (TOA) ranging. Using the PCRLB framework and existing models in the literature, we demonstrate that 2.4cm accuracy is achievable using image and RF hybrid localization with RSS ranging and 4.1mm accuracy is achiev- able with TOA ranging. The millimetric level of accuracy proves the feasibility of precise 3D reconstruction of the WCE movement path inside the small intestine. We also carried out monte carlo simulation on the PCRLB to discuss the effects of WCE movement estimation, the effects of system bandwidth as well as the effects of on-body sensor numbers and sensor placements.

Our further discussions are structured as follows. Existing literatures toward WCE localization with a particular focus on RF based and image processing based techniques are reviewed in section 5.1. Description of our WCE hybrid localization algorithm is introduced in section 5.2. The derivation of 3D posterior Cramer- Rao lower bound for the WCE hybrid localization approach is proposed in section 5.3. Most significantly, the effects of movement measurement accuracy, on-body RF receiver number, on-body RF receiver placement and RF localization system param- eters are carefully investigated and reported in section 5.4. Section 5.5 summarizes the overall work and closes our discussion.

5.1

Background.