. .. , Some public IMU-based datasets for the research of activity recognition and analysis

. .. , Some public IMU-based datasets for the research of activity recognition and analysis

.. .. Procedures,

, Filter cut-off frequency f and stance threshold h used for each activity, p.41

, Gait phase transition matrices A for the four activities, p.43

. .. , The estimated observation mean value µ, p.44

. .. , Details of each subject and activity, p.59

, Confusion matrix of batch mode recognition, using 6D observation, p.61

. .. , The sensitivity, specificity, F1 score, MCC value of the batch mode recognition by TMC-HIST, using 6D observation, p.61

. .. , Confusion matrix of the first section (up) and second section (down) of experiments, using 6D observation, p.63

, The sensitivity, specificity, F1 score, MCC value of the on-line mode recognition by TMC-HIST, using 6D observation

, Confusion matrix of batch mode recognition, using 12D observation, p.67

. .. , The sensitivity, specificity, F1 score, MCC value of the batch mode recognition by TMC-HIST, using 12D observation, p.67

. .. , Confusion matrix of the first section (up) and second section (down) of experiments, using 12D observation, p.69

, The sensitivity, specificity, F1 score, MCC value of the on-line mode recognition by TMC-HIST, using 12D observation

, Comparison of the performance w.r.t. state-of-the-art algorithms for recognising lower limb locomotion activities

, The sensitivity, specificity, F1 score, MCC value of the batch mode recognition using 6D features, for each activity of SDA dataset

, Overall performances summary of Table 5.1

, The sensitivity, specificity, F1 score, MCC value of the batch mode recognition using 12D features, for each activity of SDA dataset

.. .. , 97 5.6 The sensitivity, specificity, F1 score, MCC value of the batch mode recognition using 12D features, for each activity of LMFIMU dataset. Up: TMC-HIST; middle: TMC-GMM when ? = 9; down: SemiTMC-GMM when ? = 9

, The sensitivity, specificity, F1 score, MCC value of the on-line mode recognition using 6D features, for each activity of our own dataset

, The sensitivity, specificity, F1 score, MCC value of the on-line mode recognition using 12D features, for each activity of our own dataset

, Some wearable sensors 1

, Non-wearable sensors using cameras 2

, ZigBee-based sensor network deployed for elderly care application with the integration of mobile apps visualization, p.13

, IoT sensor network for smarthome. 3

. .. , Quantified self application related to robotics, vol.18

, 22 3.1 Right foot gait phases of walking cycle: push-up ? swing ? step down ? stance. Similar gait cycle can be deduced for other activities, such as running, stair climbing

. .. , Transition order of gait phases, ? k are the transition probability that from previous state to the current state k, p.33

, Right: the placement of the sensor on right shoe, Left: Shimmer3 IMU sensor

, Sensor acceleration of four activities for each axis, vol.39

, Sensor angular rates of four activities for each axis, i.e. ? X , ? Y , ? Z, p.39

, In each sub-figures, blue line is the filtered angular rate norm. The red, purple, cyan and green represent stance, push-up, swing and step-down, respectively

, The detected gait cycle by LR-HMC and threshold method, p.45

, The histogram of the sliding mean of angular rate, and the estimated marginal Gaussian probability density, w.r.t. the activity of walking, p.48

. .. , 2 TMC dependency graph for activity recognition, p.50

, State transition graph of the TMC-based activity recognition algorithm. The values (1,2,3,4) represent the stance, push-up, swing and step down respectively, for the four gait phases, p.50

. .. , 54 4.5 Averaged activity recognition accuracy (in %) according to ? observation and ? gait (size of stacks), for the first section of experiment (a) and for the second section (b), using 6D observation, Diagram of the adaptive on-line recognition algorithm, p.62

, Accuracy in the most recent 1000 samples w.r.t. each activities, p.64

, Recognized activities from TMC-HIST for subject 5, using 6D observation. For the first section, accuracy and MCC are 94.49% and 0.9055 respectively, while, for the second section, the values are 98.99% and 0.9800 respectively

, The detected gait cycles at the beginning of each activity (left column) compared to the ones when the estimation of the model's parameter has converged (right column)

, Averaged activity recognition accuracy (in %) according to ? observation and ? gait (size of stacks), for the first section of experiment (a) and for the second section (b), using 12D observation, p.68

, Accuracy in the most recent 1000 samples w.r.t. each activities, p.70

, Recognized activities from TMC-HIST for subject 5, using 12D observation. For the first section, accuracy and MCC are 93.25% and 0.8869 respectively, while, for the second section, the values are 98.81% and 0.9766 respectively

, The detected gait cycles at the beginning of each activity (left column) compared to the ones when the estimation of the model's parameter has converged (right column)

. .. Dependency-graph, 85 5.2 SemiTMC-GMM diagram of the traning stage, and the testing stage for both batch mode and on-line testing

, The five sensor placements in SDA dataset, the selected sensor is the one placed on the right thigh [98]

, The overall batch mode recognition accuracy using different features on SDA dataset, according to different GMM mixture number ?, p.92

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