# Sensor-Based Hand Gesture Detection and Recognition by Key Intervals
## 1 Introduction  
The Internet of Things (IoT) is a growing trend with large varieties of consumer and industrial products being connected via the Internet. To operate with these devices, the implementation of a smart human-machine interface (HMI) becomes an important issue. Some HMI systems are based on hand gesture recognition. Although vision-based recognition (VBR) algorithms [1] have been found to be effective, the information of hand gestures is extracted from video sequences captured by cameras. Because VBR systems may record users' life continuously, there are risks of personal information disclosure, which lead to privacy issues [2, 3]. In addition, high computational complexities may be required [4] for carrying out the gesture information extraction from video sequences for real-time applications. Alternatives to VBR algorithms are the sensor-based recognition (SBR) algorithms, which are based on the sensory data produced by devices different from cameras. Privacy-preserved activity recognition can then be achieved. Commonly used sensors include accelerometers [5, 6], gyroscopes [4, 7], photoplethysmography (PPG) [8], flex sensors [9], electromyography (EMG) [10], and the fusion of these sensors. Although an SBR algorithm may have lower computational complexities, it is usually difficult to extract gestures from sensory data for the subsequent classification in the algorithm. In fact, for some sensory data, a precise gesture extraction can be challenging even by direct visual inspection of the samples because gesture movements may not be easily inferred from their corresponding sensor readings. The extraction of gestures from sensory data is equivalent to the identification of the start and end points of the gestures. Dedicated sensors, explicit user actions, or special gesture markers are required for the studies in [10, 11, 12, 13] for determining the startand end locations. These methods introduce extra overhead for the gesture detection. The SBR approaches in [14; 15] carry out the gesture extraction automatically based on the variances of sensory data. However, because hand movements generally produce large variances, false alarms may be triggered as the unintended gestures are performed. In addition, accurate detection of a sequence of gestures could also be difficult. Deep learning techniques [16] such as long short-term memory (LSTM) and/or convolution neural network (CNN) have been found to be effective for the detection and classification of a sequence of gestures [7; 17; 18; 19]. However, the techniques operate under the assumptions that the start and end locations of the gesture sequences are available before each individual gesture can be identified. The goal of this study is to present a novel SBR detection and classification technique for a sequence of hand gestures. The sensors considered in this study are accelerometer and gyroscope. The proposed detection algorithm is automatic so that no dedicated sensors, explicit user actions, or special gesture markers are required. Furthermore, no prior knowledge on the start and end locations of the whole sequence of hand gestures is needed. In the proposed algorithm, each hand gesture in the sequence is regarded as an object. The detection of the object is based on a pair of key intervals. One interval, termed primary key interval (PKI), is located in the first half of the gesture. The other interval, termed secondary key interval (SKI), is in the second half. A gesture is detected when the paired key intervals are identified. The requirement for the detection of the paired key intervals is beneficial for lowering the false alarm rates triggered by unintended gestures. Furthermore, a simple automatic labelling scheme for the identification of the key intervals is proposed in this study. No manual visual inspection is required. After locating the PKI and SKI, a Gaussian-like function is then adopted for spreading the label values outside the intervals.