from utils.operation_tools import timing_decorator import numpy as np from utils.operation_tools import merge_short_gaps, remove_short_durations @timing_decorator() def detect_low_amplitude_signal(signal_data, sampling_rate, window_size_sec=1, stride_sec=None, amplitude_threshold=50, merge_gap_sec=10, min_duration_sec=5): """ 检测信号中的低幅值状态，通过计算RMS值判断信号强度是否低于设定阈值。 参数： - signal_data: numpy array，输入的信号数据 - sampling_rate: int，信号的采样率（Hz） - window_size_sec: float，分析窗口的时长（秒），默认值为 1 秒 - stride_sec: float，窗口滑动步长（秒），默认值为None（等于window_size_sec，无重叠） - amplitude_threshold: float，RMS阈值，低于此值表示低幅值状态，默认值为 50 - merge_gap_sec: float，要合并的状态之间的最大间隔（秒），默认值为 10 秒 - min_duration_sec: float，要保留的状态的最小持续时间（秒），默认值为 5 秒 返回： - low_amplitude_mask: numpy array，低幅值状态的掩码（1表示低幅值，0表示正常幅值） """ # 计算窗口大小（样本数） window_samples = int(window_size_sec * sampling_rate) # 如果未指定步长，设置为窗口大小（无重叠） if stride_sec is None: stride_sec = window_size_sec # 计算步长（样本数） stride_samples = int(stride_sec * sampling_rate) # 确保步长至少为1 stride_samples = max(1, stride_samples) # 处理信号边界，使用反射填充 pad_size = window_samples // 2 padded_signal = np.pad(signal_data, pad_size, mode='reflect') # 计算填充后的窗口数量 num_windows = max(1, (len(padded_signal) - window_samples) // stride_samples + 1) # 初始化RMS值数组 rms_values = np.zeros(num_windows) # 计算每个窗口的RMS值 for i in range(num_windows): start_idx = i * stride_samples end_idx = min(start_idx + window_samples, len(signal_data)) # 处理窗口，包括可能不完整的最后一个窗口 window_data = signal_data[start_idx:end_idx] if len(window_data) > 0: rms_values[i] = np.sqrt(np.mean(window_data ** 2)) else: rms_values[i] = 0 # 生成初始低幅值掩码：RMS低于阈值的窗口标记为1（低幅值），其他为0 low_amplitude_mask = np.where(rms_values <= amplitude_threshold, 1, 0) # 计算原始信号对应的窗口索引范围 orig_start_window = pad_size // stride_samples if stride_sec == 1: orig_end_window = orig_start_window + (len(signal_data) // stride_samples) else: orig_end_window = orig_start_window + (len(signal_data) // stride_samples) + 1 # 只保留原始信号对应的窗口低幅值掩码 low_amplitude_mask = low_amplitude_mask[orig_start_window:orig_end_window] # print("low_amplitude_mask_length: ", len(low_amplitude_mask)) num_original_windows = len(low_amplitude_mask) # 转换为时间轴上的状态序列 # 计算每个窗口对应的时间点（秒） time_points = np.arange(num_original_windows) * stride_sec # 如果需要合并间隔小的状态 if merge_gap_sec > 0: low_amplitude_mask = merge_short_gaps(low_amplitude_mask, time_points, merge_gap_sec) # 如果需要移除短时状态 if min_duration_sec > 0: low_amplitude_mask = remove_short_durations(low_amplitude_mask, time_points, min_duration_sec) low_amplitude_mask = low_amplitude_mask.repeat(stride_sec)[:len(signal_data) // sampling_rate] # 低幅值状态起止位置 [[start, end], [start, end], ...] low_amplitude_start = np.where(np.diff(np.concatenate([[0], low_amplitude_mask])) == 1)[0] low_amplitude_end = np.where(np.diff(np.concatenate([low_amplitude_mask, [0]])) == -1)[0] low_amplitude_position_list = [[start, end] for start, end in zip(low_amplitude_start, low_amplitude_end)] return low_amplitude_mask, low_amplitude_position_list def get_typical_segment_for_continues_signal(signal_data, sampling_rate=100, window_size=30, step_size=1): """ 获取十个片段 :param signal_data: 信号数据 :param sampling_rate: 采样率 :param window_size: 窗口大小（秒） :param step_size: 步长（秒） :return: 典型片段列表 """ pass # 基于体动位置和幅值的睡姿识别 # 主要是依靠体动mask，将整夜分割成多个有效片段，然后每个片段计算幅值指标，判断两个片段的幅值指标是否存在显著差异，如果存在显著差异，则认为存在睡姿变化 # 考虑到每个片段长度为10s，所以每个片段的幅值指标计算时间长度为10s，然后计算每个片段的幅值指标 # 仅对比相邻片段的幅值指标，如果存在显著差异，则认为存在睡姿变化，即每个体动相邻的30秒内存在睡姿变化，如果片段不足30秒，则按实际长度对比 @timing_decorator() def position_based_sleep_recognition(signal_data, movement_mask, sampling_rate=100, window_size_sec=30, interval_to_movement=10): # 获取有效片段起止位置 valid_mask = 1 - movement_mask valid_starts = np.where(np.diff(np.concatenate([[0], valid_mask])) == 1)[0] valid_ends = np.where(np.diff(np.concatenate([valid_mask, [0]])) == -1)[0] movement_start = np.where(np.diff(np.concatenate([[0], movement_mask])) == 1)[0] movement_end = np.where(np.diff(np.concatenate([movement_mask, [0]])) == -1)[0] segment_left_average_amplitude = [] segment_right_average_amplitude = [] segment_left_average_energy = [] segment_right_average_energy = [] # window_samples = int(window_size_sec * sampling_rate) # pad_size = window_samples // 2 # padded_signal = np.pad(signal_data, pad_size, mode='reflect') for start, end in zip(valid_starts, valid_ends): start *= sampling_rate end *= sampling_rate # 避免过短的片段 if end - start <= sampling_rate: # 小于1秒的片段不考虑 continue # 获取当前片段数据 elif end - start < (window_size_sec * interval_to_movement + 1) * sampling_rate: left_start = start left_end = min(end, left_start + window_size_sec * sampling_rate) right_start = max(start, end - window_size_sec * sampling_rate) right_end = end else: left_start = start + interval_to_movement * sampling_rate left_end = left_start + window_size_sec * sampling_rate right_start = end - interval_to_movement * sampling_rate - window_size_sec * sampling_rate right_end = end # 新的end - start确保为200的整数倍 if (left_end - left_start) % (2 * sampling_rate) != 0: left_end = left_start + ((left_end - left_start) // (2 * sampling_rate)) * (2 * sampling_rate) if (right_end - right_start) % (2 * sampling_rate) != 0: right_end = right_start + ((right_end - right_start) // (2 * sampling_rate)) * (2 * sampling_rate) # 计算每个片段的幅值指标 left_mav = np.mean(np.max(signal_data[left_start:left_end].reshape(-1, 2 * sampling_rate), axis=0)) - np.mean( np.min(signal_data[left_start:left_end].reshape(-1, 2 * sampling_rate), axis=0)) right_mav = np.mean( np.max(signal_data[right_start:right_end].reshape(-1, 2 * sampling_rate), axis=0)) - np.mean( np.min(signal_data[right_start:right_end].reshape(-1, 2 * sampling_rate), axis=0)) segment_left_average_amplitude.append(left_mav) segment_right_average_amplitude.append(right_mav) left_energy = np.sum(np.abs(signal_data[left_start:left_end] ** 2)) right_energy = np.sum(np.abs(signal_data[right_start:right_end] ** 2)) segment_left_average_energy.append(left_energy) segment_right_average_energy.append(right_energy) position_changes = [] position_change_times = [] for i in range(1, len(segment_left_average_amplitude)): # 计算幅值指标的变化率 left_amplitude_change = abs(segment_left_average_amplitude[i] - segment_left_average_amplitude[i - 1]) / max( segment_left_average_amplitude[i - 1], 1e-6) right_amplitude_change = abs(segment_right_average_amplitude[i] - segment_right_average_amplitude[i - 1]) / max( segment_right_average_amplitude[i - 1], 1e-6) # 计算能量指标的变化率 left_energy_change = abs(segment_left_average_energy[i] - segment_left_average_energy[i - 1]) / max( segment_left_average_energy[i - 1], 1e-6) right_energy_change = abs(segment_right_average_energy[i] - segment_right_average_energy[i - 1]) / max( segment_right_average_energy[i - 1], 1e-6) # 判断是否存在显著变化 (可根据实际情况调整阈值) threshold_amplitude = 0.1 # 幅值变化阈值 threshold_energy = 0.1 # 能量变化阈值 # 如果左右通道中的任一通道同时满足幅值和能量的变化阈值，则认为存在姿势变化 left_significant_change = (left_amplitude_change > threshold_amplitude) and ( left_energy_change > threshold_energy) right_significant_change = (right_amplitude_change > threshold_amplitude) and ( right_energy_change > threshold_energy) if left_significant_change or right_significant_change: # 记录姿势变化发生的时间点 用当前分割的体动的起始位置和结束位置表示 position_changes.append(1) position_change_times.append((movement_start[i - 1], movement_end[i - 1])) else: position_changes.append(0) # 0表示不存在姿势变化 # print(i,movement_start[i], movement_end[i], round(left_amplitude_change, 2), round(right_amplitude_change, 2), round(left_energy_change, 2), round(right_energy_change, 2)) return position_changes, position_change_times