prev = fl[i] t[prev][fl[i]] += 1.0 prev = fl[i] sum_ += 1.0 t = t / sum_ print(t) ######################################## f = 0 tests = 0 skf = StratifiedKFold(fl, n_folds=folds, shuffle=False, random_state=None) for train_index, test_index in skf: cl = HMM(name="Gait") distros = [] hmm_states = [] state_names = ['swing', 'stance'] positive_data = [] negative_data = [] for i in range(0, len(fl)): if fl[i] == 1: positive_data.append(fd[i]) else: negative_data.append(fd[i]) posdis = MGD.from_samples(positive_data) st = State(posdis, name='swing') distros.append(st)
def build_dis_classifier(self): skf = StratifiedKFold(self.full_labels, n_folds=self.folds) classifier_array = [] stats_array = [] num_class = len(self.full_data[0]) print (num_class) for cl in range(0, num_class): lel = -1 tp_total = 0.0 tn_total = 0.0 fp_total = 0.0 fn_total = 0.0 tests = 0 for train_index, test_index in skf: if lel > 0: lel -= 1 continue stats = [] distros = [] hmm_states = [] state_names = ['swing', 'stance'] swings = 0 stances = 0 for i in range(0, 2): dis = MGD.from_samples(self.class_data[i]) st = State(dis, name=state_names[i]) distros.append(dis) hmm_states.append(st) model = HMM() print(model.states) model.add_states(hmm_states) model.add_transition(model.start, hmm_states[0], 0.5) model.add_transition(model.start, hmm_states[1], 0.5) model.add_transition(hmm_states[1], model.end, 0.000000000000000001) model.add_transition(hmm_states[0], model.end, 0.000000000000000001) for i in range(0, 2): for j in range(0, 2): model.add_transition(hmm_states[i], hmm_states[j], self.t[i][j]) model.bake() tp = 0.0 tn = 0.0 fp = 0.0 fn = 0.0 train_data = self.full_data[train_index, cl] train_class = self.full_labels[train_index, cl] test_data = self.full_data[test_index] test_class = self.full_labels[test_index] print(np.isfinite(train_data).all()) print(np.isfinite(test_data).all()) print(np.isnan(train_data.any())) print(np.isinf(train_data.any())) print(np.isnan(test_data.any())) print(np.isinf(test_data.any())) if (not np.isfinite(train_data.any())) or (not np.isfinite(test_data.any())) \ or (not np.isfinite(train_class.any())) or (not np.isfinite(test_data.any())): rospy.logerr("NaN or Inf Detected") exit() try: rospy.logwarn("Training model #"+str(cl)+", fold #" + str(tests)) seq = np.array(train_data) model.fit(seq, algorithm='baum-welch', verbose='True', n_jobs=8, max_iterations=150) except ValueError: rospy.logwarn("Something went wrong, exiting") rospy.shutdown() exit() seq = [] if self.batch_test == 1: s = 0 # for s in range(0, len(test_data)): while s < len(test_data): k = 0 seq_entry = [] while k < 20 and s < len(test_data): seq_entry.append(test_data[s]) k += 1 s += 1 seq.append(seq_entry) else: seq = np.array(test_data) if seq == [] or test_data == []: rospy.logerr("Empty testing sequence") continue log, path = model.viterbi(test_data) if (len(path) - 2) != len(test_data): rospy.logerr(len(path)) rospy.logerr(path[0][1].name) rospy.logerr(path[len(path) - 1][1].name) rospy.logerr(len(test_data)) exit() tests += 1 for i in range(0, len(path) - 2): if path[i + 1][1].name != 'Gait-start' and path[i + 1][1].name != 'Gait-end': if path[i + 1][1].name == 'swing': # prediction is 0 swings += 1 if test_class[i] == 0: # class is 0 tn += 1.0 elif test_class[i] == 1: fn += 1.0 # class is 1 elif path[i + 1][1].name == 'stance': # prediction is 1 stances += 1 if test_class[i] == 1: # class is 1 tp += 1.0 elif test_class[i] == 0: # class is 0 fp += 1.0 print (swings) print (stances) if (tp + fn) != 0.0: rospy.logwarn("Sensitivity : " + str(tp / (tp + fn))) # sensitivity = tp / (tp + fn) else: rospy.logwarn("Sensitivity : 0.0") # sensitivity = 0.0 if (tn + fp) != 0.0: rospy.logwarn("Specificity : " + str(tn / (tn + fp))) # specificity = tn_total / (tn_total + fp_total) else: rospy.logwarn("Specificity : 0.0") # specificity = 0.0 if (tn + tp + fn + fp) != 0.0: rospy.logwarn("Accuracy : " + str((tn + tp) / (tn + tp + fn + fp))) # accuracy = (tn + tp) / (tn + tp + fn + fp) else: rospy.logwarn("Accuracy : 0.0") # accuracy = 0.0 tn_total += tn tp_total += tp fn_total += fn fp_total += fp tp_total /= tests tn_total /= tests fp_total /= tests fn_total /= tests rospy.logerr("TP :" + str(tp_total)) rospy.logerr("TN :" + str(tn_total)) rospy.logerr("FP :" + str(fp_total)) rospy.logerr("FN :" + str(fn_total)) rospy.logerr("Tests :" + str(tests)) if (tp_total + fn_total) != 0.0: sensitivity = tp_total / (tp_total + fn_total) else: sensitivity = 0.0 if (tn_total + fp_total) != 0.0: specificity = tn_total / (tn_total + fp_total) else: specificity = 0.0 if (tn_total + tp_total + fn_total + fp_total) != 0.0: accuracy = (tn_total + tp_total) / (tn_total + tp_total + fn_total + fp_total) else: accuracy = 0.0 rospy.logwarn("----------------------------------------------------------") rospy.logerr("Total accuracy: " + str(accuracy)) rospy.logerr("Total sensitivity: " + str(sensitivity)) rospy.logerr("Total specificity: " + str(specificity)) stats = [tn_total * tests, fn_total * tests, fp_total * tests, fn_total * tests, tests, accuracy, sensitivity, specificity] rospy.logwarn("-------------------DONE-------------------------") classifier_array.append(model) stats_array.append(stats) pickle.dump(classifier_array, open(datafile + "distributed_classifiers.p", 'wb')) pickle.dump(stats_array, open(datafile + "distributed_stats.p", 'wb')) scio.savemat(datafile + "distributed_stats.mat", {'stats': stats_array})
def main(): rospy.init_node('hmm_trainer') phase_pub = rospy.Publisher('/phase', Int32, queue_size=10) rospack = rospkg.RosPack() packpath = rospack.get_path('exo_control') datapath = packpath + "/log/mat_files/" verbose = rospy.get_param('~verbose', False) """Print console output into text file""" sys.stdout = open(packpath + "/log/results/leave-one-out_cross_validation_cov.txt", "w") """Data loading""" n_trials = 3 n_sub = 9 healthy_subs = ["daniel", "erika", "felipe", "jonathan", "luis", "nathalia", "paula", "pedro", "tatiana"] patients = ["andres", "carlos", "carmen", "carolina", "catalina", "claudia", "emmanuel", "fabian", "gustavo"] study_subs = [healthy_subs, patients] dataset = [{} for x in range(len(study_subs))] for i in range(len(study_subs)): for sub in study_subs[i]: dataset[i][sub] = {"gyro_y": [[] for x in range(n_trials)], "fder_gyro_y": [[] for x in range(n_trials)], "time": [[] for x in range(n_trials)], "labels": [[] for x in range(n_trials)], "Fs_fsr": 0.0} for group in dataset: for sub,data in group.iteritems(): for trial in range(n_trials): mat_file = scio.loadmat(datapath + sub + "_proc_data" + str(trial+1) + ".mat") for signal in data: if signal not in ["pathol","fder_gyro_y"]: if signal == "Fs_fsr": data[signal] = mat_file[signal][0][0] else: data[signal][trial] = mat_file[signal][0] del mat_file """Feature extraction""" """First derivative""" for group in dataset: for sub,data in group.iteritems(): for trial in range(n_trials): der = [] gyro_y = data["gyro_y"][trial] der.append(gyro_y[0]) for i in range(1,len(gyro_y)-1): der.append((gyro_y[i+1]-gyro_y[i-1])/2) der.append(gyro_y[-1]) data["fder_gyro_y"][trial] = der del der, sub, data """Global variables of cHMM""" startprob = [0.25, 0.25, 0.25, 0.25] state_names = ['hs', 'ff', 'ho', 'sw'] n_classes = 4 n_signals = 2 tol = 6e-2 # Tolerance window of 60 ms # pathology = 0 for pathology in range(len(dataset)): if pathology == 0: rospy.logwarn("**Leave-one-out cross validation with HEALTHY subjects**") print "**Leave-one-out cross validation with HEALTHY subjects**" else: rospy.logwarn("**Leave-one-out cross validation with PATIENTS**") print "**Leave-one-out cross validation with PATIENTS**" # if True: for lou_sub,lou_data in dataset[pathology].iteritems(): # Iterate through leave-one-out subject's data rospy.logwarn("Leave " + lou_sub + " out:") print "Leave " + lou_sub + " out:" t = np.zeros((4, 4)) # Transition matrix prev = -1 for trial in range(n_trials): for label in lou_data["labels"][trial]: if prev == -1: prev = label t[prev][label] += 1.0 prev = label t = normalize(t, axis=1, norm='l1') if verbose: rospy.logwarn("TRANSITION MATRIX\n" + str(t)) class_data = [[] for x in range(n_classes)] # full_lou_data = [] # full_lou_labels = [] for trial in range(n_trials): for sample in range(len(lou_data["gyro_y"][trial])): d = [lou_data["gyro_y"][trial][sample], lou_data["fder_gyro_y"][trial][sample]] l = lou_data["labels"][trial][sample] # full_lou_data.append(d) # full_lou_labels.append(l) class_data[l].append(d) """Multivariate Gaussian Distributions for each hidden state""" class_means = [[[] for x in range(n_signals)] for i in range(n_classes)] class_vars = [[[] for x in range(n_signals)] for i in range(n_classes)] class_std = [[[] for x in range(n_signals)] for i in range(n_classes)] class_cov = [] for state in range(n_classes): cov = np.ma.cov(np.array(class_data[state]), rowvar=False) class_cov.append(cov) for signal in range(n_signals): class_means[state][signal] = np.array(class_data[state][:])[:, [signal]].mean(axis=0) class_vars[state][signal] = np.array(class_data[state][:])[:, [signal]].var(axis=0) class_std[state][signal] = np.array(class_data[state][:])[:, [signal]].std(axis=0) # lou_trial = 1 # if True: for lou_trial in range(n_trials): rospy.logwarn("Trial {}".format(lou_trial+1)) print("Trial {}".format(lou_trial+1)) """Classifier initialization""" # distros = [] hmm_states = [] for state in range(n_classes): dis = MGD\ (np.array(class_means[state]).flatten(), np.array(class_cov[state])) st = State(dis, name=state_names[state]) # distros.append(dis) hmm_states.append(st) model = HMM(name="Gait") model.add_states(hmm_states) """Initial transitions""" for state in range(n_classes): model.add_transition(model.start, hmm_states[state], startprob[state]) """Left-right model""" for i in range(n_classes): for j in range(n_classes): model.add_transition(hmm_states[i], hmm_states[j], t[i][j]) model.bake() """Create training and test data""" x_train = [] x_test = [] test_gyro_y = lou_data["gyro_y"][lou_trial] test_fder_gyro_y = lou_data["fder_gyro_y"][lou_trial] """Create test data with n-th trial of leave-one-out subject""" for sample in range(len(test_gyro_y)): x_test.append([test_gyro_y[sample], test_fder_gyro_y[sample]]) """Create training data with n-1 trials of the rest of subjects (healthy group)""" for train_sub,train_data in dataset[0].iteritems(): count_trials = 0 if lou_sub != train_sub: # if train_sub == "daniel": for trial in range(n_trials): if trial != lou_trial and count_trials < 1: # rospy.logwarn(trial) train_gyro_y = train_data["gyro_y"][trial] train_fder_gyro_y = train_data["fder_gyro_y"][trial] for sample in range(len(train_gyro_y)): x_train.append([train_gyro_y[sample], train_fder_gyro_y[sample]]) count_trials += 1 rospy.logwarn(len(x_train)) x_train = list([x_train]) """Training""" rospy.logwarn("Training HMM...") model.fit(x_train, algorithm='baum-welch', verbose=True) # model.fit(x_train, algorithm='viterbi', verbose='True') """Find most-likely sequence""" rospy.logwarn("Finding most-likely sequence...") logp, path = model.viterbi(x_test) # rospy.logwarn(len(path)) # rospy.logwarn(len(lou_data["labels"][lou_trial])) class_labels = [] for i in range(len(lou_data["labels"][lou_trial])): path_phase = path[i][1].name for state in range(n_classes): if path_phase == state_names[state]: class_labels.append(state) '''Saving classifier labels into csv file''' # np.savetxt(packpath+"/log/inter_labels/"+lou_sub+"_labels.csv", class_labels, delimiter=",", fmt='%s') # rospy.logwarn("csv file with classifier labels was saved.") # lou_data["labels"][lou_trial] = lou_data["labels"][lou_trial][1:] """Calculate mean time (MT) of stride and each gait phase and Coefficient of Variation (CoV)""" rospy.logwarn("Mean time (MT) and Coefficient of Variance (CoV)") print "Mean time (MT) and Coefficient of Variance (CoV)" curr_label = -1 count = 0 n_phases = 0 stride_samples = 0 phases_time = [[] for x in range(n_classes)] stride_time = [] for label in class_labels: if curr_label != label: n_phases += 1 stride_samples += count if label == 0: # Gait start: HS if n_phases == 4: # If a whole gait cycle has past stride_time.append(stride_samples/lou_data["Fs_fsr"]) n_phases = 0 stride_samples = 0 phases_time[label-1].append(count/lou_data["Fs_fsr"]) curr_label = label count = 1 else: count += 1.0 for phase in range(n_classes): mean_time = np.mean(phases_time[phase]) phase_std = np.std(phases_time[phase]) rospy.logwarn("(" + state_names[phase] + ")") print "(" + state_names[phase] + ")" rospy.logwarn("Mean time: " + str(mean_time) + " + " + str(phase_std)) print "Mean time: " + str(mean_time) + " + " + str(phase_std) rospy.logwarn("CoV: " + str(phase_std/mean_time*100.0)) print("CoV: " + str(phase_std/mean_time*100.0)) mean_time = np.mean(stride_time) phase_std = np.std(stride_time) rospy.logwarn("(Stride)") print "(Stride)" rospy.logwarn("Mean time: " + str(mean_time) + " + " + str(phase_std)) print "Mean time: " + str(mean_time) + " + " + str(phase_std) rospy.logwarn("CoV: " + str(phase_std/mean_time*100.0)) print("CoV: " + str(phase_std/mean_time*100.0))
distros = [] hmm_states = [] state_names = ['swing', 'stance'] hmm_states = [] for i in range(0, 2): dis = MGD.from_samples(class_data[i]) st = State(dis, name=state_names[i]) distros.append(dis) hmm_states.append(st) skf = StratifiedKFold(full_labels, n_folds=folds) for train_index, test_index in skf: model = HMM(name="Gait") hmm_states = [] for i in range(0, 2): # dis = MGD(np.array(class_means[i]).flatten(), np.array(class_cov[i])) dis = MGD.from_samples(class_data[i]) st = State(dis, name=state_names[i]) distros.append(dis) hmm_states.append(st) model.add_states(hmm_states) model.add_transition(model.start, hmm_states[0], 0.5) model.add_transition(model.start, hmm_states[1], 0.5) for i in range(0, 2): for j in range(0, 2):
class_vars[i][j] = np.array(class_data[i][:])[:, [j]].var(axis=0) class_std[i][j] = np.array(class_data[i][:])[:, [j]].std(axis=0) rospy.logwarn("Class means shape :" + str(np.array(class_means).shape)) rospy.logwarn("Class variances shape :" + str(np.array(class_vars).shape)) rospy.logwarn("Class covariances shape :" + str(np.array(class_cov).shape)) distros = [] hmm_states = [] for i in range(0, n_classes): dis = MGD(np.array(class_means[i]).flatten(), np.array(class_cov[i])) st = State(dis, name=phase_labels[i]) distros.append(dis) hmm_states.append(st) model = HMM(name="Gait") model.add_states(hmm_states) model.add_transition(model.start, hmm_states[0], 0.5) model.add_transition(model.start, hmm_states[1], 0.5) t = normalize(t, axis=1, norm='l1') for i in range(0, n_classes): for j in range(0, n_classes): model.add_transition(hmm_states[i], hmm_states[j], t[i][j]) print(hmm_states[i].name + "(" + str(i) + ")-> " + hmm_states[j].name + "(" + str(j) + ") : " + str(t[i][j])) model.bake() # seq = list([full_features[:limit]])
def main(): rospy.init_node('hmm_trainer') param_vec = [] rospack = rospkg.RosPack() if (len(sys.argv) < 2): print("Missing the prefix argument.") exit() else: prefix = sys.argv[1] use_measurements = np.zeros(3) # patient = rospy.get_param('~patient', 'None') # if prefix == 'None': # rospy.logerr("No filename given ,exiting") # exit() phase_pub = rospy.Publisher('/phase', Int32, queue_size=10) packpath = rospack.get_path('exo_gait_phase_det') datapath = packpath + "/log/mat_files/" rospy.logwarn("Patient: {}".format(prefix)) print("Patient: {}".format(prefix)) verbose = rospy.get_param('~verbose', False) """Print console output into text file""" # sys.stdout = open(packpath + "/log/results/intra-sub_" + prefix + ".txt", "w") """Data loading""" n_trials = 3 data = [[] for x in range(0, n_trials)] for i in range(0, n_trials): data[i] = scio.loadmat(datapath + prefix + "_proc_data" + str(i + 1) + ".mat") accel_x = [[] for x in range(0, n_trials)] accel_y = [[] for x in range(0, n_trials)] accel_z = [[] for x in range(0, n_trials)] gyro_x = [[] for x in range(0, n_trials)] gyro_y = [[] for x in range(0, n_trials)] gyro_z = [[] for x in range(0, n_trials)] time_array = [[] for x in range(0, n_trials)] labels = [[] for x in range(0, n_trials)] fs_fsr = [] for i in range(0, n_trials): # accel_x[i] = data[i]["accel_x"][0] # accel_y[i] = data[i]["accel_y"][0] # accel_z[i] = data[i]["accel_z"][0] gyro_x[i] = data[i]["gyro_x"][0] gyro_y[i] = data[i]["gyro_y"][0] gyro_z[i] = data[i]["gyro_z"][0] time_array[i] = data[i]["time"][0] labels[i] = data[i]["labels"][0] fs_fsr.append(data[i]["Fs_fsr"][0][0]) """Feature extraction""" """First derivative""" # fder_gyro_x = [] # for i in range(n_trials): # der = [] # der.append(gyro_x[i][0]) # for j in range(1,len(gyro_x[i])-1): # der.append((gyro_x[i][j+1]-gyro_x[i][j-1])/2) # der.append(gyro_x[i][-1]) # fder_gyro_x.append(der) fder_gyro_y = [] for i in range(n_trials): der = [] der.append(gyro_y[i][0]) for j in range(1, len(gyro_y[i]) - 1): der.append((gyro_y[i][j + 1] - gyro_y[i][j - 1]) / 2) der.append(gyro_y[i][-1]) fder_gyro_y.append(der) # fder_gyro_z = [] # for i in range(n_trials): # der = [] # der.append(gyro_z[i][0]) # for j in range(1,len(gyro_z[i])-1): # der.append((gyro_z[i][j+1]-gyro_z[i][j-1])/2) # der.append(gyro_z[i][-1]) # fder_gyro_z.append(der) """Second derivative""" # sder_gyro_x = [] # for i in range(n_trials): # der = [] # der.append(fder_gyro_x[i][0]) # for j in range(1,len(fder_gyro_x[i])-1): # der.append((fder_gyro_x[i][j+1]-fder_gyro_x[i][j-1])/2) # der.append(fder_gyro_x[i][-1]) # sder_gyro_x.append(der) # # sder_gyro_y = [] # for i in range(n_trials): # der = [] # der.append(fder_gyro_y[i][0]) # for j in range(1,len(fder_gyro_y[i])-1): # der.append((fder_gyro_y[i][j+1]-fder_gyro_y[i][j-1])/2) # der.append(fder_gyro_y[i][-1]) # sder_gyro_y.append(der) # # sder_gyro_z = [] # for i in range(n_trials): # der = [] # der.append(fder_gyro_z[i][0]) # for j in range(1,len(fder_gyro_z[i])-1): # der.append((fder_gyro_z[i][j+1]-fder_gyro_z[i][j-1])/2) # der.append(fder_gyro_z[i][-1]) # sder_gyro_z.append(der) """Peak detector""" # window_wid = 15 # Window width should be odd # search_ratio = window_wid/2 # pdet_gyro_x = [] # for i in range(n_trials): # pdet = [] # for j in range(len(gyro_x[i])): # if j <= search_ratio: # win = gyro_x[i][:j+search_ratio+1] # elif j >= len(gyro_x[i])-search_ratio-1: # win = gyro_x[i][j-search_ratio:] # else: # win = gyro_x[i][j-search_ratio:j+search_ratio+1] # pdet.append(gyro_x[i][j]/max(win)) # pdet_gyro_x.append(pdet) # print len(gyro_x) # print len(pdet_gyro_x) # for i in range(3): # print len(gyro_x[i]) # print len(pdet_gyro_x[i]) # pdet_gyro_y = [] # for i in range(n_trials): # pdet = [] # for j in range(len(gyro_y[i])): # if j <= search_ratio: # win = gyro_y[i][:j+search_ratio+1] # elif j >= len(gyro_y[i])-search_ratio-1: # win = gyro_y[i][j-search_ratio:] # else: # win = gyro_y[i][j-search_ratio:j+search_ratio+1] # pdet.append(gyro_y[i][j]/max(win)) # pdet_gyro_y.append(pdet) # # pdet_gyro_z = [] # for i in range(n_trials): # pdet = [] # for j in range(len(gyro_z[i])): # if j <= search_ratio: # win = gyro_z[i][:j+search_ratio+1] # elif j >= len(gyro_z[i])-search_ratio-1: # win = gyro_z[i][j-search_ratio:] # else: # win = gyro_z[i][j-search_ratio:j+search_ratio+1] # pdet.append(gyro_z[i][j]/max(win)) # pdet_gyro_z.append(pdet) """Create training and test data""" ff = [[] for x in range(0, n_trials)] for j in range(0, n_trials): for k in range(0, len(time_array[j])): f_ = [] # f_.append(accel_x[j][k]) # f_.append(accel_y[j][k]) # f_.append(accel_z[j][k]) # f_.append(gyro_x[j][k]) # f_.append(fder_gyro_x[j][k]) # f_.append(sder_gyro_x[j][k]) # f_.append(pdet_gyro_x[j][k]) f_.append(gyro_y[j][k]) f_.append(fder_gyro_y[j][k]) # f_.append(sder_gyro_y[j][k]) # f_.append(pdet_gyro_y[j][k]) # f_.append(gyro_z[j][k]) # f_.append(fder_gyro_z[j][k]) # f_.append(sder_gyro_z[j][k]) # f_.append(pdet_gyro_z[j][k]) ff[j].append(f_) n_signals = len(ff[0][0]) """cHMM""" startprob = [0.25, 0.25, 0.25, 0.25] state_names = ['hs', 'ff', 'ho', 'sw'] rospy.logwarn("""Intra-subject training""") print("""Intra-subject training""") # for leave_one_out in range(0, n_trials): for leave_one_out in range(1, 2): rospy.logwarn("-------TRIAL {}-------".format(leave_one_out + 1)) print("-------TRIAL {}-------".format(leave_one_out + 1)) """Transition matrix""" t = np.zeros((4, 4)) # Transition matrix prev = -1 for i in range(0, len(labels[leave_one_out])): # data[i]._replace(label = correct_mapping[data[i].label]) if prev == -1: prev = labels[leave_one_out][i] t[prev][labels[leave_one_out][i]] += 1.0 prev = labels[leave_one_out][i] t = normalize(t, axis=1, norm='l1') if verbose: rospy.logwarn("TRANSITION MATRIX\n" + str(t)) n_classes = 4 class_data = [[] for x in range(n_classes)] full_data = [] full_labels = [] for i in range(len(ff[leave_one_out])): full_data.append(ff[leave_one_out][i]) full_labels.append(labels[leave_one_out][i]) # print full_data == ff[leave_one_out] # print full_labels == labels[leave_one_out] # print len(full_data) == len(full_labels) # for i in range(0,len(ff[leave_one_out-1])): # full_data.append(ff[leave_one_out-1][i]) # full_labels.append(labels[leave_one_out-1][i]) # for i in range(0,len(ff[(leave_one_out+1) % n_trials])): # full_data.append(ff[(leave_one_out+1) % n_trials][i]) # full_labels.append(labels[(leave_one_out+1) % n_trials][i]) # print len(full_data) == (len(ff[leave_one_out]) + len(ff[leave_one_out-1]) + len(ff[(leave_one_out+1) % n_trials])) # print full_data # print len(full_data) # print full_labels # print len(full_labels) for i in range(0, len(full_data)): class_data[full_labels[i]].append(full_data[i]) """Multivariate Gaussian Distributions for each hidden state""" class_means = [[[] for x in range(n_signals)] for i in range(n_classes)] class_vars = [[[] for x in range(n_signals)] for i in range(n_classes)] class_std = [[[] for x in range(n_signals)] for i in range(n_classes)] class_cov = [] classifiers = [] for i in range(0, n_classes): # cov = np.ma.cov(np.array(class_data[i]), rowvar=False) cov = np.cov(np.array(class_data[i]), rowvar=False) class_cov.append(cov) for j in range(0, n_signals): class_means[i][j] = np.array( class_data[i][:])[:, [j]].mean(axis=0) class_vars[i][j] = np.array(class_data[i][:])[:, [j]].var(axis=0) class_std[i][j] = np.array(class_data[i][:])[:, [j]].std(axis=0) print "\n" + str(class_cov) + "\n" """Classifier initialization""" distros = [] hmm_states = [] for i in range(n_classes): dis = MGD\ (np.array(class_means[i]).flatten(), np.array(class_cov[i])) st = State(dis, name=state_names[i]) distros.append(dis) hmm_states.append(st) model = HMM(name="Gait") model.add_states(hmm_states) """Initial transitions""" for i in range(0, n_classes): model.add_transition(model.start, hmm_states[i], startprob[i]) """Left-right model""" for i in range(0, n_classes): for j in range(0, n_classes): model.add_transition(hmm_states[i], hmm_states[j], t[i][j]) model.bake() # print (model.name) # rospy.logwarn("N. observations: " + str(model.d)) # print (model.edges) # rospy.logwarn("N. hidden states: " + str(model.silent_start)) # print model """Training""" # limit = int(len(ff1)*(8/10.0)) # 80% of data to test, 20% to train # x_train = list([ff1[:limit]]) # x_train = list([ff1,ff2]) # x_train = list([ff2]) x_train = [] for i in range(0, len(ff[leave_one_out - 1])): x_train.append(ff[leave_one_out - 1][i]) for i in range(0, len(ff[(leave_one_out + 1) % n_trials])): x_train.append(ff[(leave_one_out + 1) % n_trials][i]) x_train = list([x_train]) rospy.logwarn("Training...") model.fit(x_train, algorithm='baum-welch', verbose=verbose) # model.fit(list([ff[leave_one_out-1]]), algorithm='baum-welch', verbose=verbose) # model.fit(list([ff[(leave_one_out+1) % n_trials]]), algorithm='baum-welch', verbose=verbose) # model.fit(seq, algorithm='viterbi', verbose='True') """Find most-likely sequence""" # logp, path = model.viterbi(ff[limit:]) logp, path = model.viterbi(ff[leave_one_out]) # print logp # print path class_labels = [] for i in range(len(labels[leave_one_out])): path_phase = path[i][1].name for state in range(n_classes): if path_phase == state_names[state]: class_labels.append(state) labels[leave_one_out] = list(labels[leave_one_out][1:]) # Saving classifier labels into csv file # np.savetxt(packpath+"/log/intra_labels/"+prefix+"_labels"+str(leave_one_out+1)+".csv", class_labels, delimiter=",", fmt='%s') # rospy.logwarn("csv file with classifier labels was saved.") sum = 0.0 true_pos = 0.0 false_pos = 0.0 true_neg = 0.0 false_neg = 0.0 tol = 6e-2 # Tolerance window of 60 ms tol_window = int((tol / 2) / (1 / float(fs_fsr[leave_one_out]))) print "FSR freq: " + str(fs_fsr[leave_one_out]) print "Tolerance win: " + str(tol_window) # print tol_window # # print type(tol_window) # for i in range(0, len(labels[leave_one_out])): # """Tolerance window""" # if i > tol_window+1 and i < len(labels[leave_one_out])-tol_window: # # curr_tol = time_array[leave_one_out][i+tol_window]-time_array[leave_one_out][i-tol_window] # # print curr_tol # win = [] # for j in range(i-tol_window,i+tol_window+1): # win.append(state_names[labels[leave_one_out][j]]) # if path[i][1].name in win: # sum += 1.0 # else: # if path[i][1].name == labels[leave_one_out][i]: # sum += 1.0 """Performance Evaluation""" rospy.logwarn("Calculating results...") time_error = [[] for x in range(n_classes)] for phase in range(n_classes): for i in range(len(labels[leave_one_out])): """Tolerance window""" if i >= tol_window and i < len( labels[leave_one_out]) - tol_window: # curr_tol = time_array[leave_one_out][i+tol_window]-time_array[leave_one_out][i-tol_window] # print curr_tol win = [] for j in range(i - tol_window, i + tol_window + 1): win.append(labels[leave_one_out][j]) """Calculate time error with true positives""" if class_labels[i] == phase: if class_labels[i] in win: for k in range(len(win)): if win[k] == phase: time_error[phase].append( (k - tol_window) / fs_fsr[leave_one_out]) break true_pos += 1.0 if verbose: print phase + ", " + state_names[labels[ leave_one_out][i]] + ", " + class_labels[ i] + ", true_pos" else: false_pos += 1.0 if verbose: print phase + ", " + state_names[labels[ leave_one_out][i]] + ", " + class_labels[ i] + ", false_pos" else: if phase != labels[leave_one_out][i]: # if phase not in win: true_neg += 1.0 if verbose: print phase + ", " + state_names[labels[ leave_one_out][i]] + ", " + class_labels[ i] + ", true_neg" else: false_neg += 1.0 if verbose: print phase + ", " + state_names[labels[ leave_one_out][i]] + ", " + class_labels[ i] + ", false_neg" else: if class_labels[i] == phase: if class_labels[i] == labels[leave_one_out][i]: true_pos += 1.0 else: false_pos += 1.0 else: if phase != labels[leave_one_out][i]: true_neg += 1.0 else: false_neg += 1.0 rospy.logwarn("Timing error") print("Timing error") for phase in range(n_classes): rospy.logwarn("(" + state_names[phase] + ")") print "(" + state_names[phase] + ")" if len(time_error[phase]) > 0: rospy.logwarn( str(np.mean(time_error[phase])) + " + " + str(np.std(time_error[phase]))) print str(np.mean(time_error[phase])) + " + " + str( np.std(time_error[phase])) else: rospy.logwarn("0.06 + 0") print "0.06 + 0" """Calculate mean time (MT) of stride and each gait phase and Coefficient of Variation (CoV)""" rospy.logwarn("Mean time (MT) and Coefficient of Variance (CoV)") print("Mean time (MT) and Coefficient of Variance (CoV)") n_group = 0 for label_group in [class_labels, labels[leave_one_out]]: if n_group == 0: rospy.logwarn("Results for HMM:") print("Results for HMM:") else: rospy.logwarn("Results for FSR:") print("Results for FSR:") curr_label = -1 count = 0 n_phases = 0 stride_samples = 0 phases_time = [[] for x in range(n_classes)] stride_time = [] for label in label_group: # for label in class_labels: if curr_label != label: n_phases += 1 stride_samples += count if label == 0: # Gait start: HS if n_phases == 4: # If a whole gait cycle has past stride_time.append(stride_samples / fs_fsr[leave_one_out]) n_phases = 0 stride_samples = 0 phases_time[label - 1].append(count / fs_fsr[leave_one_out]) curr_label = label count = 1 else: count += 1.0 for phase in range(n_classes): mean_time = np.mean(phases_time[phase]) phase_std = np.std(phases_time[phase]) rospy.logwarn("(" + state_names[phase] + ")") print "(" + state_names[phase] + ")" rospy.logwarn("Mean time: " + str(mean_time) + " + " + str(phase_std)) print "Mean time: " + str(mean_time) + " + " + str(phase_std) rospy.logwarn("CoV: " + str(phase_std / mean_time * 100.0)) print("CoV: " + str(phase_std / mean_time * 100.0)) mean_time = np.mean(stride_time) phase_std = np.std(stride_time) rospy.logwarn("(Stride)") print "(Stride)" rospy.logwarn("Mean time: " + str(mean_time) + " + " + str(phase_std)) print "Mean time: " + str(mean_time) + " + " + str(phase_std) rospy.logwarn("CoV: " + str(phase_std / mean_time * 100.0)) print("CoV: " + str(phase_std / mean_time * 100.0)) n_group += 1 """Accuracy""" # acc = sum/len(labels[leave_one_out]) if (true_neg + true_pos + false_neg + false_pos) != 0.0: acc = (true_neg + true_pos) / (true_neg + true_pos + false_neg + false_pos) else: acc = 0.0 """Sensitivity or True Positive Rate""" if true_pos + false_neg != 0: tpr = true_pos / (true_pos + false_neg) else: tpr = 0.0 """Specificity or True Negative Rate""" if false_pos + true_neg != 0: tnr = true_neg / (false_pos + true_neg) else: tnr = 0.0 # rospy.logwarn("Accuracy: {}%".format(acc*100)) rospy.logwarn("Accuracy: {}%".format(acc * 100.0)) # print("Accuracy: {}%".format(acc*100.0)) rospy.logwarn("Sensitivity: {}%".format(tpr * 100.0)) # print("Sensitivity: {}%".format(tpr*100.0)) rospy.logwarn("Specificity: {}%".format(tnr * 100.0)) # print("Specificity: {}%".format(tnr*100.0)) """Goodness index""" G = np.sqrt((1 - tpr)**2 + (1 - tnr)**2) if G <= 0.25: rospy.logwarn("Optimum classifier (G = {} <= 0.25)".format(G)) # print("Optimum classifier (G = {} <= 0.25)".format(G)) elif G > 0.25 and G <= 0.7: rospy.logwarn("Good classifier (0.25 < G = {} <= 0.7)".format(G)) # print("Good classifier (0.25 < G = {} <= 0.7)".format(G)) elif G == 0.7: rospy.logwarn("Random classifier (G = 0.7)") # print("Random classifier (G = 0.7)") else: rospy.logwarn("Bad classifier (G = {} > 0.7)".format(G))
def main(): rospy.init_node('hmm_trainer') phase_pub = rospy.Publisher('/phase', Int32, queue_size=10) rospack = rospkg.RosPack() packpath = rospack.get_path('exo_control') datapath = packpath + "/log/mat_files/" verbose = rospy.get_param('~verbose', False) """Print console output into text file""" sys.stdout = open(packpath + "/log/results/leave-one-out_cross_validation.txt", "w") """Data loading""" n_trials = 3 n_sub = 9 healthy_subs = ["daniel", "erika", "felipe", "jonathan", "luis", "nathalia", "paula", "pedro", "tatiana"] patients = ["andres", "carlos", "carmen", "carolina", "catalina", "claudia", "emmanuel", "fabian", "gustavo"] study_subs = [healthy_subs, patients] dataset = [{} for x in range(len(study_subs))] for i in range(len(study_subs)): for sub in study_subs[i]: dataset[i][sub] = {"gyro_y": [[] for x in range(n_trials)], "fder_gyro_y": [[] for x in range(n_trials)], "time": [[] for x in range(n_trials)], "labels": [[] for x in range(n_trials)], "Fs_fsr": 0.0} for group in dataset: for sub,data in group.iteritems(): for trial in range(n_trials): mat_file = scio.loadmat(datapath + sub + "_proc_data" + str(trial+1) + ".mat") for signal in data: if signal not in ["pathol","fder_gyro_y"]: if signal == "Fs_fsr": data[signal] = mat_file[signal][0][0] else: data[signal][trial] = mat_file[signal][0] del mat_file """Feature extraction""" """First derivative""" for group in dataset: for sub,data in group.iteritems(): for trial in range(n_trials): der = [] gyro_y = data["gyro_y"][trial] der.append(gyro_y[0]) for i in range(1,len(gyro_y)-1): der.append((gyro_y[i+1]-gyro_y[i-1])/2) der.append(gyro_y[-1]) data["fder_gyro_y"][trial] = der del der, sub, data """Global variables of cHMM""" startprob = [0.25, 0.25, 0.25, 0.25] state_names = ['hs', 'ff', 'ho', 'sw'] n_classes = 4 n_signals = 2 tol = 6e-2 # Tolerance window of 60 ms # for pathology in range(len(dataset)): # if pathology == 0: # rospy.logwarn("**Leave-one-out cross validation with HEALTHY subjects**") # print "**Leave-one-out cross validation with HEALTHY subjects**" # else: # rospy.logwarn("**Leave-one-out cross validation with PATIENTS**") # print "**Leave-one-out cross validation with PATIENTS**" if True: # for lou_sub,lou_data in dataset[pathology].iteritems(): # Iterate through leave-one-out subject's data for lou_sub,lou_data in dataset[0].iteritems(): # Iterate through leave-one-out subject's data rospy.logwarn("Leave " + lou_sub + " out:") print "Leave " + lou_sub + " out:" t = np.zeros((4, 4)) # Transition matrix prev = -1 for trial in range(n_trials): for label in lou_data["labels"][trial]: if prev == -1: prev = label t[prev][label] += 1.0 prev = label t = normalize(t, axis=1, norm='l1') if verbose: rospy.logwarn("TRANSITION MATRIX\n" + str(t)) class_data = [[] for x in range(n_classes)] full_lou_data = [] full_lou_labels = [] for trial in range(n_trials): for sample in range(len(lou_data["gyro_y"][trial])): d = [lou_data["gyro_y"][trial][sample], lou_data["fder_gyro_y"][trial][sample]] l = lou_data["labels"][trial][sample] full_lou_data.append(d) full_lou_labels.append(l) class_data[l].append(d) """Multivariate Gaussian Distributions for each hidden state""" class_means = [[[] for x in range(n_signals)] for i in range(n_classes)] class_vars = [[[] for x in range(n_signals)] for i in range(n_classes)] class_std = [[[] for x in range(n_signals)] for i in range(n_classes)] class_cov = [] classifiers = [] for state in range(n_classes): cov = np.ma.cov(np.array(class_data[state]), rowvar=False) class_cov.append(cov) for signal in range(n_signals): class_means[state][signal] = np.array(class_data[state][:])[:, [signal]].mean(axis=0) class_vars[state][signal] = np.array(class_data[state][:])[:, [signal]].var(axis=0) class_std[state][signal] = np.array(class_data[state][:])[:, [signal]].std(axis=0) """Classifier initialization""" distros = [] hmm_states = [] for state in range(n_classes): dis = MGD\ (np.array(class_means[state]).flatten(), np.array(class_cov[state])) st = State(dis, name=state_names[state]) distros.append(dis) hmm_states.append(st) model = HMM(name="Gait") model.add_states(hmm_states) """Initial transitions""" for state in range(n_classes): model.add_transition(model.start, hmm_states[state], startprob[state]) """Left-right model""" for i in range(n_classes): for j in range(n_classes): model.add_transition(hmm_states[i], hmm_states[j], t[i][j]) model.bake() """Create training and test data""" x_train = [] x_test = [] test_gyro_y = lou_data["gyro_y"][-1] test_fder_gyro_y = lou_data["fder_gyro_y"][-1] """Create test data with n-th trial of leave-one-out subject""" for sample in range(len(test_gyro_y)): x_test.append([test_gyro_y[sample], test_fder_gyro_y[sample]]) """Create training data with n-1 trials of all subjects (patients group)""" # if pathology == 1: # for trial in range(n_trials-1): # train_gyro_y = lou_data["gyro_y"][trial] # train_fder_gyro_y = lou_data["fder_gyro_y"][trial] # for sample in range(len(train_gyro_y)): # x_train.append([train_gyro_y[sample], train_fder_gyro_y[sample]]) """Create training data with n-1 trials of the rest of subjects (healthy group)""" # for train_sub,train_data in dataset[pathology].iteritems(): for train_sub,train_data in dataset[0].iteritems(): # if lou_sub != train_sub: if True: for trial in range(n_trials-1): train_gyro_y = train_data["gyro_y"][trial] train_fder_gyro_y = train_data["fder_gyro_y"][trial] for sample in range(len(train_gyro_y)): x_train.append([train_gyro_y[sample], train_fder_gyro_y[sample]]) x_train = list([x_train]) """Training""" rospy.logwarn("Training HMM...") model.fit(x_train, algorithm='baum-welch', verbose=True) # model.fit(x_train, algorithm='viterbi', verbose='True') """Find most-likely sequence""" logp, path = model.viterbi(x_test) class_labels = [] for i in range(len(lou_data["labels"][-1])): path_phase = path[i][1].name for state in range(n_classes): if path_phase == state_names[state]: class_labels.append(state) # Saving classifier labels into csv file np.savetxt(packpath+"/log/inter_labels/"+lou_sub+"_labels.csv", class_labels, delimiter=",", fmt='%s') rospy.logwarn("csv file with classifier labels was saved.") lou_data["labels"][-1] = lou_data["labels"][-1][1:] """Results""" sum = 0.0 true_pos = 0.0 false_pos = 0.0 true_neg = 0.0 false_neg = 0.0 tol_window = int((tol/2) / (1/float(lou_data["Fs_fsr"]))) rospy.logwarn("Calculating results...") time_error = [[] for x in range(n_classes)] for phase in range(n_classes): for i in range(len(lou_data["labels"][-1])): """Tolerance window""" if i >= tol_window and i < len(lou_data["labels"][-1])-tol_window: win = [] for win_label in lou_data["labels"][-1][i-tol_window:i+tol_window+1]: win.append(win_label) if class_labels[i] == phase: if class_labels[i] in win: for k in range(len(win)): if win[k] == phase: time_error[phase].append((k-tol_window)/lou_data["Fs_fsr"]) break true_pos += 1.0 if verbose: print phase + ", " + lou_data["labels"][-1][i] + ", " + class_labels[i] + ", true_pos" else: false_pos += 1.0 if verbose: print phase + ", " + lou_data["labels"][-1][i] + ", " + class_labels[i] + ", false_pos" else: if phase != lou_data["labels"][-1][i]: # if phase not in win: true_neg += 1.0 if verbose: print phase + ", " + lou_data["labels"][-1][i] + ", " + class_labels[i] + ", true_neg" else: false_neg += 1.0 if verbose: print phase + ", " + lou_data["labels"][-1][i] + ", " + class_labels[i] + ", false_neg" else: if class_labels[i] == phase: if class_labels[i] == lou_data["labels"][-1][i]: true_pos += 1.0 else: false_pos += 1.0 else: if phase != lou_data["labels"][-1][i]: true_neg += 1.0 else: false_neg += 1.0 rospy.logwarn("Timing error") print "Timing error" for phase in range(n_classes): rospy.logwarn("(" + state_names[phase] + ")") print "(" + state_names[phase] + ")" if len(time_error[phase]) > 0: rospy.logwarn(str(np.mean(time_error[phase])) + " + " + str(np.std(time_error[phase]))) print str(np.mean(time_error[phase])) + " + " + str(np.std(time_error[phase])) else: rospy.logwarn("0.06 + 0") print "0.06 + 0" """Calculate mean time (MT) of stride and each gait phase and Coefficient of Variation (CoV)""" rospy.logwarn("Mean time (MT) and Coefficient of Variance (CoV)") print "Mean time (MT) and Coefficient of Variance (CoV)" n_group = 0 for label_group in [class_labels, lou_data["labels"][-1]]: if n_group == 0: rospy.logwarn("Results for HMM:") print "Results for HMM:" else: rospy.logwarn("Results for FSR:") print "Results for FSR:" curr_label = -1 count = 0 n_phases = 0 stride_samples = 0 phases_time = [[] for x in range(n_classes)] stride_time = [] for label in label_group: # for label in class_labels: if curr_label != label: n_phases += 1 stride_samples += count if label == 0: # Gait start: HS if n_phases == 4: # If a whole gait cycle has past stride_time.append(stride_samples/lou_data["Fs_fsr"]) n_phases = 0 stride_samples = 0 phases_time[label-1].append(count/lou_data["Fs_fsr"]) curr_label = label count = 1 else: count += 1.0 for phase in range(n_classes): mean_time = np.mean(phases_time[phase]) phase_std = np.std(phases_time[phase]) rospy.logwarn("(" + state_names[phase] + ")") print "(" + state_names[phase] + ")" rospy.logwarn("Mean time: " + str(mean_time) + " + " + str(phase_std)) print "Mean time: " + str(mean_time) + " + " + str(phase_std) rospy.logwarn("CoV: " + str(phase_std/mean_time*100.0)) print("CoV: " + str(phase_std/mean_time*100.0)) mean_time = np.mean(stride_time) phase_std = np.std(stride_time) rospy.logwarn("(Stride)") print "(Stride)" rospy.logwarn("Mean time: " + str(mean_time) + " + " + str(phase_std)) print "Mean time: " + str(mean_time) + " + " + str(phase_std) rospy.logwarn("CoV: " + str(phase_std/mean_time*100.0)) print("CoV: " + str(phase_std/mean_time*100.0)) n_group += 1 """Accuracy""" if (true_neg+true_pos+false_neg+false_pos) != 0.0: acc = (true_neg + true_pos)/(true_neg + true_pos + false_neg + false_pos) else: acc = 0.0 """Sensitivity or True Positive Rate""" if true_pos+false_neg != 0: tpr = true_pos / (true_pos+false_neg) else: tpr = 0.0 """Specificity or True Negative Rate""" if false_pos+true_neg != 0: tnr = true_neg / (false_pos+true_neg) else: tnr = 0.0 rospy.logwarn("Accuracy: {}%".format(acc*100.0)) print("Accuracy: {}%".format(acc*100.0)) rospy.logwarn("Sensitivity: {}%".format(tpr*100.0)) print("Sensitivity: {}%".format(tpr*100.0)) rospy.logwarn("Specificity: {}%".format(tnr*100.0)) print("Specificity: {}%".format(tnr*100.0)) """Goodness index""" G = np.sqrt((1-tpr)**2 + (1-tnr)**2) if G <= 0.25: rospy.logwarn("Optimum classifier (G = {} <= 0.25)".format(G)) print("Optimum classifier (G = {} <= 0.25)".format(G)) elif G > 0.25 and G <= 0.7: rospy.logwarn("Good classifier (0.25 < G = {} <= 0.7)".format(G)) print("Good classifier (0.25 < G = {} <= 0.7)".format(G)) elif G == 0.7: rospy.logwarn("Random classifier (G = 0.7)") print("Random classifier (G = 0.7)") else: rospy.logwarn("Bad classifier (G = {} > 0.7)".format(G)) print("Bad classifier (G = {} > 0.7)".format(G)) del test_gyro_y, test_fder_gyro_y, train_gyro_y, train_fder_gyro_y, d, l
def __init__(self, n_trials=3, leave_one_out=1): """Variable initialization""" self.patient = rospy.get_param("gait_phase_det/patient") self.verbose = rospy.get_param("gait_phase_det/verbose") self.n_trials = n_trials self.n_features = 2 # Raw data and 1st-derivative self.leave_one_out = leave_one_out self.rec_data = 0.0 # Number of recorded IMU data self.proc_data = 0.0 # Number of extracted features self.win_size = 3 self.raw_win = [None] * self.win_size # self.fder_win = [0] * self.win_size self.ff = [[] for x in range(self.n_trials)] # Training and test dataset self.labels = [[] for x in range(self.n_trials)] # Reference labels from local data self.first_eval = True self.model_loaded = False algorithm = rospy.get_param("gait_phase_det/algorithm") rospy.loginfo('Decoding algorithm: {}'.format(algorithm)) if algorithm not in DECODER_ALGORITHMS: raise ValueError("Unknown decoder {!r}".format(algorithm)) self.decode = { "fov": self._run_fov, "bvsw": self._run_bvsw }[algorithm] self.imu_callback = { "fov": self._fov_callback, "bvsw": self._bvsw_callback }[algorithm] """HMM variables""" ''' State list: s1: Heel Strike (HS) s2: Flat Foot (FF) s3: Heel Off (HO) s4: Swing Phase (SP)''' self.model_name = "Gait" self.has_model = False self.must_train = False self.states = ['s1', 's2', 's3', 's4'] self.n_states = len(self.states) self.state2phase = {"s1": "hs", "s2": "ff", "s3": "ho", "s4": "sp"} self.train_data = [] self.mgds = {} self.dis_means = [[] for x in range(self.n_states)] self.dis_covars = [[] for x in range(self.n_states)] self.start_prob = [1.0/self.n_states]*self.n_states self.trans_mat = np.array([(0.9, 0.1, 0, 0), (0, 0.9, 0.1, 0), (0, 0, 0.9, 0.1), (0.1, 0, 0, 0.9)]) # Left-right model # self.trans_mat = np.array([0.8, 0.1, 0, 0.1], [0.1, 0.8, 0.1, 0], [0, 0.1, 0.8, 0.1], [0.1, 0, 0.1, 0.8]) # Left-right-left model self.log_startprob = [] self.log_transmat = np.empty((self.n_states, self.n_states)) self.max_win_len = 11 # ms (120 ms: mean IC duration for healthy subjects walking at comfortable speed) self.viterbi_path = np.empty((self.max_win_len+1, self.n_states)) self.backtrack = [[None for x in range(self.n_states)] for y in range(self.max_win_len+1)] self.global_path = [] self.work_buffer = np.empty(self.n_states) self.boundary = 1 self.buff_len = 0 self.states_pos = {} for i in range(len(self.states)): self.states_pos[self.states[i]] = i self.last_state = -1 self.curr_state = -1 self.conv_point = 0 self.conv_found = False self.smp_freq = 100.0 # Hz self.fp_thresh = 1/self.smp_freq*4 # Threshold corresponds to 8 samples self.time_passed = 0.0 self.obs = [[None for x in range(self.n_features)] for y in range(self.max_win_len)] self.model = HMM(name=self.model_name) """ROS init""" rospy.init_node('real_time_HMM', anonymous=True) rospack = rospkg.RosPack() self.packpath = rospack.get_path('hmm_gait_phase_classifier') self.init_subs() self.init_pubs() """HMM-training (if no model exists)""" try: '''HMM-model loading''' with open(self.packpath+'/log/HMM_models/'+self.patient+'.txt') as infile: json_model = json.load(infile) self.model = HMM.from_json(json_model) rospy.logwarn(self.patient + "'s HMM model was loaded.") self.has_model = True except IOError: if os.path.isfile(self.packpath + "/log/mat_files/" + self.patient + "_proc_data1.mat"): """Training with data collected with FSR-based reference system""" self.data_ext = 'mat' self.must_train = True elif os.path.isfile(self.packpath + "/log/IMU_data/" + self.patient + "_labels.csv"): """Training with data collected with offline threshold-based gait phase detection method""" self.data_ext = 'csv' self.must_train = True else: rospy.logerr("Please collect data for training ({})!".format(self.patient)) if self.must_train: rospy.logwarn("HMM model not trained yet for {}!".format(self.patient)) rospy.logwarn("Training HMM with local data...") self.load_data() self.init_hmm() self.train_hmm() self.has_model = True if self.has_model: try: '''MGDs loading if model exists''' for st in self.states: with open(self.packpath+'/log/HMM_models/'+self.patient+'_'+self.state2phase[st]+'.txt') as infile: yaml_dis = yaml.safe_load(infile) dis = MGD.from_yaml(yaml_dis) self.mgds[st] = dis rospy.logwarn(self.patient +"'s " + self.state2phase[st] + " MGC was loaded.") '''Loading means and covariance matrix''' self.dis_means[self.states_pos[st]] = self.mgds[st].parameters[0] self.dis_covars[self.states_pos[st]] = self.mgds[st].parameters[1] except yaml.YAMLError as exc: rospy.logwarn("Not able to load distributions: " + exc) """Transition and initial (log) probabilities matrices upon training""" trans_mat = self.model.dense_transition_matrix()[:self.n_states,:self.n_states] if self.verbose: print '**TRANSITION MATRIX (post-training)**\n'+ str(trans_mat) for i in range(self.n_states): self.log_startprob.append(ln(self.start_prob[i])) for j in range(self.n_states): self.log_transmat[i,j] = ln(trans_mat[i][j]) self.model_loaded = True