From: Multi-sensor fusion based on multiple classifier systems for human activity identification
Authors | Activities | Sensors/position | Algorithms/evaluation | Fusion type | Strength | Weakness |
---|---|---|---|---|---|---|
Tolstikov [34], Amoretti et al. [35], Sebak et al. [38], Tunca et al. [10], Qui et al. [4] | Static: sitting, standing, lying, sleeping, idle Mobility based: walking, climbing stairs, leaving home Household: prepare breakfast, prepare dinner, drink Daily hygiene: shower, use toilet | Sensors: Camera, accelerometer, gyroscope, magnetometer, binary sensor Position: ankle, toilet flush, cupboard Sampling rate: ~ 100 Hz | Algorithm: dynamic Bayesian network, Kalman filtering, Dempster–Shafer Evaluation metrics: error rates, accuracy, computation time, precision, recall, F-measure | Data fusion | Provide a simple and real-time, computationally efficient and independent implementation of human activity recognition | Inability to handle a long sequence of activities. Moreover, the approach is sensitive to sensor position, noise and sometimes impractical to implement |
Nishida et al. [47], Spinsante et al. [5], Shoaib et al. [9], Xu et al. [50], Chen and Wang [8], Berenguar et al. (2017), Zdravevski et al. [74], Fong et al. [45], Dobbins et al. [6], Köping et al. [46], San-Segundo et al. [48], Li et al. [49], Pires et al. [29] | Static: sitting, lying down, standing, reading, making calls, kneeling Mobility based: walking, jogging, ascending descending stairs, biking, stretching, object-lifting, bending, falling forward, falling left, falling backward, talking, cycling, nordic walking, jumping, car, running Household: eating, watching TV, open door, close door, open fridge, close fridge, open dishwasher, close dishwasher, open drawer, close drawer, close table, drink form cup, vacuuming, ironing, shopping, cooking Daily hygiene: brush teeth, bathing, washing clothes, drying clothes, house cleaning Transition activities: stand up from sitting, stand up from laying, going up, going down, laying down from standing Office: Typing, Writing, walking at computer Harmful habit: smoking | Sensor: accelerometer, gyroscope, magnetometer, linear acceleration, gravity, heart rate, location sensor, air pressure, video Position: front pocket, chest, ankle, thigh, forearm, wrist, waist, back, feet, Right shoulder Sampling rate: 20–200 Hz | Algorithms: k-NN, ANN, DT, SVM, NB, LR, RF, Hoeffding tree, HMM, RNN, CNN, LSTM, LDC, QDC, POLYC, PARZENC, Gaussian Mixture Model, MLP, FNN, DNN Evaluation metric: AUC, accuracy, precision, recall, F-measure, specificity, computation time, error rate, Kappa, FP, FN, Confusion Matrix | Feature fusion | Use to fuse sensor of diverse modalities and less sensitive to noise | Feature incompatibility, instability to sensor failure and signal variation reduce performance |
Catal et al. [52], Gjoreski et al. [27], Peng et al. [54], Chowdhury et al. [19], Garcia-Ceja et al. [18], Saha et al. [16], Peng et al. [59] | Static: lying, sitting, standing, kneeling, sleeping, recreational activities Mobility based activities: running, walking, cycling, ascending stairs, descending stairs, jogging, travel, sports Household: washing dishes, mop floor, sweep the floor, eat chips, watch TV, shopping, brush teeth Hygiene based: cleaning, wash hand Office activities: working on the computer, meeting | Sensor: accelerometer, calorimeter, biosensor, body-media, accelerometer, sound, accelerometer, location data, vital signs Positions: chest, wrist, mouth, pocket, table (sound), shirt pocket, belt, bag sampling rate: 20–200 Hz | Base classifiers: MLR, SVR, GPR, M5P, MLP, SVM, RF, BDT, DNN, Adaboost, LR, J48, KNN Fusion method: posterior probability, majority voting, single classifier based multi-view stacking Evaluation metrics: F-measure, recall, precision, accuracy, Confusion Matrix | Multiple classifier systems | Can handle complex activity details, high dimensional sensor data and uncertainty by systematic classifier fusion. Combine heterogeneous and homogeneous classifier to reduce variance and ambiguity that are likely to occur in the single classifier | External knowledge dependencies and may be computationally complex based on the base classifier |