# A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
## 1 Introduction
SenseIO [24] designed a ubiquitous multi-model system to fuse cell tower, Wi-Fi-based, activity recognition and light intensity data based on fixed detection rules for IO detection and their experiments for each module and all framework scenarios show that the SenseIO provides promising detection accuracy (above 92%). In [25], utilizing the fixed rules, light sensor, magnetic sensor and satellite signals were integrated to identify the IO status to help achieve seamless indoor and outdoor positioning. However, fixed detection rules or threshold-based methods are difficult to adapt to different environments and devices. In the second category, features are extracted from smartphone embedded sensors and detected IO status by the machine learning algorithm. SenseMe [26] utilized C4.5 algorithm on data generated from GPS, gyroscope, accelerometer and the Bluetooth module sense environmental context, as well as the context-aware location. They evaluated SenseMe against several metrics with the aid of 2 two-week long live deployments involving 15 participants and the detection accuracy reached 91.23%. [PERSON] et al. [27] proposed a sound-based IO detection method that utilized acoustic features created by different patterns of reverberations according to ambient environments. Then, [PERSON] leveraged a binary classification method to determine the IO environments by using the acoustic feature. Considering the electromagnetic environments are different, the data characteristics of the magnetic sensors under IO situations are different. The experiments were conducted at the KAIST campus locatedin Daejeon, South Korea and the best accuracy (96.79%) was achieved when the score calculation range was 50, and the threshold value, 2000. In addition, the transition time of their method took only 3.81 s on average. [PERSON] et al. (2018) employed a binary classification technique on the received signal strength indicator (RSSI) from 802.11 access points to identify a pedestrian's indoor or outdoor status. They conducted experiments on their campus, with a mean error rate around 2.5%. MagIO ([PERSON] et al., 2018) utilized machine learning algorithms including Support Vector Machines (SVM), Gradient Boosting Machines (GBM), Random Forest (RF), K-Nearest Neighbor (kNN) and Decision Trees (DT) to deal with magnetic signals for IO detection. Experiments showed that Naive Bayes and random forest possess the capability to achieve an accuracy of 80% and higher with magnetic data alone. An ensemble-based stacking approach is presented, as well, which achieves an accuracy of 85.30% for a campus area, shopping mall and subway station using three different smartphones. [PERSON] et al. (2018) applied a machine learning algorithm to classify the neighboring GSM station's signal in different environments and identify the users' current context by signal recognition. They test the algorithm in four different environments in their campus. The results show that their algorithm is capable of identifying open outdoors, semi-outdoors, light indoors and deep indoors environments with 100% accuracy using four nearby GSM stations' signal strength. [PERSON] (2018) considered employing co-training according to the feature of light, magnetic and cell sensors for detection. It can automatically learn characteristics of new environments and devices and thereby provides a detection accuracy exceeding 90% even in unfamiliar circumstances. [PERSON] (2018) leveraged J48 and other machine learning algorithms to detect the IO state. They utilized multiple contextual features such as activity, barometric, ambient light, GSM, magnetometer variance, etc. Using all sensors; they could achieve 99% classification accuracy with a 10-fold cross-validation test. Wi-FiBoost (2018) was designed to utilize AdaBoost (2018) determines in a fast and accurate way whether a device is inside or outside particular buildings. They conducted all their experiments in two facilities located on their campus and showed that the resulting performance, a mean error rate around 2.5%.