Table 1.
Overview of records reviewed
| Study | Type / Group | Sensor selection | Sensor placement | Concept description |
|---|---|---|---|---|
| Vallery et al. | IES / 1 | 2 x angle & angular | C: hip & knee | Mapping function for control of knee prototype |
| (P, 2011) [10] | velocity sensors | with estimated contralateral limb motion data. | ||
| Bernal-Torres et al. | IES / 1 | 1 x IMU | C: thigh | Active biomimic polycentric knee prototype with |
| (H, 2018) [11, 12] | contralateral echo-control strategy. | |||
| Su et al. | IES / 1 | 3 x IMUs | C: thigh, shank & | Intent recognition system based on |
| (P, 2019)[13] | ankle | convolutional neural network classification. | ||
| CYBERLEGs | IES / 1 | 2 x pressure insoles | B: shoes inlays | Finite-state control of a powered ankle-knee |
| project series 1 | 7 x IMUs | B: thighs, shanks, | coupled prototype using whole-body aware | |
| (P, 2017) [15–18] | feet & 1 x trunk | noninvasive, distributed wireless sensor control. | ||
| Hu et at. | IES / 2 | 4 x IMUs | B: thighs & shank | Classification error reduction through fusion of |
| (P, 2018) [19–21] | 4 x GONIOs | B: knee & ankle | bilateral lower-limb neuromechanical signals, | |
| Extended by: | 14 x EMGs | B: leg muscles | providing feasibility & benchmark datasets. | |
| Krausz et al. | EES / 2 | 1 x IMU | On the waist in | Adding vision features to the prior |
| (H, 2019) [22] | 1 x depth camera | a belt construction | concept improving the classification. | |
| Hu et al. | IES / 3 | 1 x IMU | I: thigh | Bilateral gait segmentation from ipsilateral depth |
| (H, 2018)[23] | 1 x depth camera | sensor with the contralateral leg in field of view. | ||
| Zhang et al. | IES / 3 | 1 x depth camera | On the waist | Depth signal from legs as input to an |
| (H, 2018) [25] | with tilt angle | oscillator-based gait phase estimator. | ||
| Scandaroli et al. | EES / 4 | 2 x gyroscopes | Built into a | Infrared distance sensor setup for estimation |
| (T, 2010) [27] | 4 x infrared sensors | foot prototype | of foot orientation with respect to ground. | |
| Ishikawa et al. | EES / 4 | 2 x infrared sensors | Left & right on | Infrared distance sensor setup for estimation |
| (H, 2018) [28] | 1 x IMU | one normal shoe | of foot clearance with respect to ground. | |
| Kleiner et al. | EES / 5 | 1 x motion tracking | I: between artificial | Concept and prototype of a foresighted |
| (T, 2011) [29] | 1 x laser scanner | ankle & knee joint | control system using a 2D laser scanner. | |
| Huang’s group 2 | EES / 5 | 1 x IMU | I: lateral side | Terrain recognition based on laser distance, |
| (P, 2016) [30–33] | 1 x laser sensor | of the trunk | motion estimation and geometric constrains. | |
| Carvalho et al. | EES / 5 | 1 x laser sensor | On the waist | Terrain recognition based on laser distance |
| (H, 2019) [36] | with 45° tilt angle | information and geometric constrains. | ||
| Sahoo et al. | EES / 5 | 3/4 x range sensors | I: On the shank & | Array of distance sensors for geometry-based |
| (H, 2019) [37] | 1 x force resistor | on the heel of the foot | obstacle recognition in front of the user. | |
| Varol et al. and | EES / 5 | 1 x depth camera | I: shank | Intent recognition framework using a single |
| Massalin et al. | depth camera and a cubic kernel support | |||
| (H, 2018) [38, 39] | vector machine for real-time classification. | |||
| Laschowski et al. | EES / 5 | 1 x color camera | Wearable | Terrain identification based on color images |
| (H, 2019) [40] | chest-mounting | and deep convolutional network classification. | ||
| Yan et al. | EES / 5 | 1 x depth camera | On the trunk | Locomotion mode estimation based on depth |
| (H, 2018) [41] | in 1.06m height | feature extraction and finite-state classification. | ||
| Diaz et al. | EES / 5 | 1 x IMU | I: foot & shin | Terrain context identification and inclination |
| (H, 2018) [43] | 1 x color camera | estimation based on color image classification. | ||
| Krausz et al. | EES / 5 | 1 x depth camera | Fixed in 1.5m height | Stair segmentation strategy from depth |
| (H, 2015) [45] | 1 x accelerometer | with -50° tilt angle | sensing information of the environment. | |
| Kleiner et al. | EES / 5 | 1 x IMU | I: thigh | Stair detection algorithm through fusion of |
| (P, 2018) [46] | 1 x radar sensor | motion trajectory and radar distance data. | ||
| Zhang et al. | EES / 5 | 1 x IMU | I: knee lateral | Environmental feature extraction based on |
| (P, 2019) [47, 48] | 1 x depth camera | neural network depth scene classification. |