*3.1. Common Methodology*

In view of the sensing and processing techniques for HAR, the methods can be described with a common methodology as shown in Figure 2, which may be applicable for both optical tracking and wearable sensing techniques [55,56]. The common methodology consists of four steps: data acquisition and pre-processing, segmentation, feature extraction, and classification [57].

**Figure 2.** Common methodology for HAR.


### *3.2. Modeling of Human Body Parts for HAR*

Since the human body is a systematic whole, a primary step in using HAR is to build a mechanical and posture kinematic model for the motion of human body parts. Human skeleton joints are the key features for optical tracking based HAR methods, where markers are sometimes applied for human gesture and posture recognition as well as motion tracking [53,58]. Figure 3 shows a skeleton-based multi-section model for the hand and human body. For wearable IMU-based HAR, the multi-segment model of human skeleton is also used as a basis to build a kinematic model of body segments. For example, the kinematic model of an elbow joint can be estabished with two segments connected by two revolute joints, allowing two degrees of freedom (DoF), as shown in Figure 4 [59]; the DoF kinematic model of the left leg, including three DoF angle joints, one DoF knee joint, and three DoF hip joints, is presented in [60], and the kinematics of the ankle to the hip and trunk is discussed in [61]. With the multi-segment model and kinematic model, the posture and the motion of human body parts and the whole body can be estimated with the angle and position data obtained with sensor devices.

**Figure 3.** Multi-segments model for human body: (**a**) hand skeletal model, (**b**) whole body skeletal model.

**Figure 4.** Kinematic model of elbow joint.

### *3.3. Identifiable Human Activities*

Since human body parts are not rigid structures and are, instead, structures that deform and move in different ways following complex kinematic patterns, human activity parameters that belong to these parts are very hard to measure. Many new technical solutions are customized for human activity measurment, and remarkable progress has been made. The identifiable human activities are summarized in Table 1.


**Table 1.** Identifiable human activities and example applications.


**Table 1.** *Cont.*

As shown in Table 1, many human body parts and the human body as a whole can be used for activity recognition for different purposes, including fingers, hand, arm, elbow, knee, ankle, and the whole body. Of course, the identifiable human activities and the corresponding applications are not limited to those listed in the table. Rapid technical progress has resulted in the continual addition of new possibilities and the creation of new applications in this particular field.

### **4. Novel Sensing Techniques**

The appropriate measurement techniques and quality sensor data are the fundamentals for effective HAR and further applications. In this section, the sensing techniques for HAR are summarized with taxonomy and compared with proposed indexes. The sensor network solutions for wireless data collection and multi-sensor nodes for whole body monitoring are also discussed.

### *4.1. Taxonomy and Evaluation*

According to the principles of sensor devices, the sensing techniques can be divided into six categories: optical tracking, radio frequency techniques, acoustic techniques, inertial measurement, force sensors, and EMG sensors. Each of the above categories may include a couple of different sensing techniques that may exhibit different performances in dealing with HAR applications. The feasibility of sensing techniques for HAR can be evaluated with five indicators: convenience of use, application scenarios, richness of information, the quality of raw data, and cost. The discussion and analysis of the sensing techniques are conducted with the abovementioned categories and evaluation indicators, which are as shown in Figure 5.

**Figure 5.** Taxonomy and evaluation indexes for HAR sensing techniques.

### *4.2. Sensing Techniques with Example Applications*

To give a comprehensive overview and in-depth discussion of the sensing techniques for HAR and related studies, the sensing techniques of the five types and the corresponding typical applications are summarized in Table 2.



1 IR-UWB, 2 UHF, and 3 FSR are the abbreviations of impulse radio ultra-wide band, ultra-high frequency, and force sensitive resistor, respectively.


weaknesses are the monotonousness and implicity of information provided by the sensors which requires specialized processing.


On account of the di fferent sensing techniques, they may behave di fferently with respect to the di fferent evaluation indexes. In summary, for the convenience of use, the optical tracking and radar techniques are non-contact, but they are normally installed on particular locations. The inertial sensors can be wearable and provide rich useful information for human activity analysis, but contact measurement is involved, which may introduce inconvenience for users' daily activities. As a contact measurement method, force sensors and EMG can reveal information of body part local areas, which is competitive in medical care.

### *4.3. Body Area Sensor Networks*

For the wearable sensing techniques, it is important to carry out the data collection without interrupting people's normal activities. Therefore, power cords data wires should be replaced with miniature batteries and wireless communication nodes. The optional wireless communication techniques which are commonly used include Bluetooth, WiFi, BLE, Zigbee, 4G/5G, NB-IoT, and LoRa, etc. [97] Since 4G/5G, NB-IoT, and LoRa are commonly used for long range data transmission for di fferent purposes, Bluetooth, WiFi, BLE, and Zigbee are more likely to be chosen for short range wearable communications. A comparison of the wireless techniques is given in Table 3.

Since wearable sensing devices are powered with batteries, the energy consumption, data rate, and network topology are the key parameters to be considered when establishing the systems. Normally, Bluetooth is used for the data collection of single sensor devices: For example, Bluetooth is employed to transmit IMU data for sports analysis [7], force data of piezoelectric sensors for gait recognition [89], and IMU data for assessment of elbow spasticity [98]. Then, BLE and ZigBee are competitive in building a BAN for the data collection of multiple nodes systems; ZigBee is used for multiple IMU data collection in [66], while BLE-based wireless BAN (WBAN) was used for joint angle estimation in [99].


**Table 3.** Alternative wireless communication standards for HAR.
