鸿蒙分布式睡眠监测系统：多设备协同的声音分析与睡眠质量评估原创

进修的泡芙

发布于 2025-6-22 17:45

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鸿蒙分布式睡眠监测系统：多设备协同的声音分析与睡眠质量评估

一、系统架构设计

!https://example.com/harmonyos-sleep-arch.png

采用四层架构：
感知层：多设备麦克风阵列与环境传感器

分析层：分布式声音特征提取与睡眠阶段识别

同步层：多源数据融合与结果聚合

应用层：可视化报告与智能建议

二、核心模块实现
声音特征分析模块

// SleepAudioAnalyzer.ts
import audio from ‘@ohos.multimedia.audio’;
import soundAnalysis from ‘@ohos.ai.soundAnalysis’;
import distributedData from ‘@ohos.data.distributedData’;

interface SleepAudioFeature {
timestamp: number;
deviceId: string;
snoreIntensity: number; // 打鼾强度(0-1)
movementScore: number; // 翻身动作评分(0-1)
breathingRate: number; // 呼吸频率(次/分钟)
environmentNoise: number; // 环境噪音分贝
export class SleepAudioAnalyzer {

private audioCapturer?: audio.AudioCapturer;
private analyzer: soundAnalysis.SoundAnalyzer;
private kvManager: distributedData.KVManager;

async init() {
// 初始化声音分析器
this.analyzer = await soundAnalysis.createAnalyzer({
models: [‘snore_detection’, ‘breathing_rate’],
sampleRate: 16000
});

// 初始化分布式数据同步
const context = getContext(this);
this.kvManager = distributedData.createKVManager({ context });

async startMonitoring(): Promise<AsyncIterable<SleepAudioFeature>> {

this.audioCapturer = await audio.createAudioCapturer({
  streamInfo: {
    samplingRate: audio.AudioSamplingRate.SAMPLE_RATE_16K,
    channels: audio.AudioChannel.CHANNEL_1,
    format: audio.AudioFormat.FORMAT_PCM_16BIT

});

this.audioCapturer.start();

return {
  [Symbol.asyncIterator]: async function* () {
    while (this.isActive) {
      const buffer = await this.audioCapturer?.read();
      if (buffer) {
        const features = await this.processAudio(buffer);
        yield features;
        await this.syncFeatures(features);

}

  }.bind(this)
};

private async processAudio(buffer: ArrayBuffer): Promise<SleepAudioFeature> {

const result = await this.analyzer.analyze(buffer);
return {
  timestamp: Date.now(),
  deviceId: 'local_device',
  snoreIntensity: result.models.snore_detection?.intensity || 0,
  movementScore: this.calculateMovementScore(result.rawFeatures),
  breathingRate: result.models.breathing_rate?.rate || 12,
  environmentNoise: result.volume
};

// 其他方法…

睡眠阶段识别模块

// SleepStageClassifier.ts
import statistical from ‘@ohos.ai.statistical’;
import neuralNetwork from ‘@ohos.ai.neuralNetwork’;

export class SleepStageClassifier {
private model?: neuralNetwork.Model;

async init() {
this.model = await neuralNetwork.loadModel({
modelPath: ‘models/sleep_stage.model’,
device: ‘NPU’
});
async classify(features: SleepAudioFeature[]): Promise<SleepStage> {

const input = this.prepareInputTensor(features);
const output = await this.model?.predict(input);
return this.parseOutput(output);

private prepareInputTensor(features: SleepAudioFeature[]): neuralNetwork.Tensor {

// 将30秒窗口的特征数据转换为模型输入
const windowSize = 30; // 30秒窗口
const featureSize = 4; // 4个特征维度

const data = new Float32Array(windowSize * featureSize);
features.slice(-windowSize).forEach((f, i) => {
  data[i * featureSize] = f.snoreIntensity;
  data[i * featureSize + 1] = f.movementScore;
  data[i * featureSize + 2] = f.breathingRate;
  data[i * featureSize + 3] = f.environmentNoise;
});

return new neuralNetwork.Tensor({
  dataType: 'float32',
  shape: [1, windowSize, featureSize],
  data
});

// 其他方法…

主页面实现（ArkUI）

// SleepMonitorApp.ets
import { SleepAudioAnalyzer } from ‘./SleepAudioAnalyzer’;
import { SleepStageClassifier } from ‘./SleepStageClassifier’;

@Entry
@Component
struct SleepMonitorApp {
@State currentStage?: SleepStage;
@State sleepScore: number = 0;
@State features: SleepAudioFeature[] = [];
@State devices: string[] = [];

private analyzer = new SleepAudioAnalyzer();
private classifier = new SleepStageClassifier();
private monitoring = false;

async aboutToAppear() {
await this.analyzer.init();
await this.classifier.init();
this.loadConnectedDevices();
async startMonitoring() {

this.monitoring = true;

for await (const feature of this.analyzer.startMonitoring()) {
  this.features = [...this.features.slice(-3600), feature]; // 保留1小时数据
  
  // 每30秒进行一次睡眠阶段分类
  if (this.features.length % 30 === 0) {
    this.currentStage = await this.classifier.classify(this.features);
    this.sleepScore = this.calculateSleepScore();

}

build() {

Column() {
  // 睡眠状态展示
  SleepStageDisplay({
    stage: this.currentStage,
    score: this.sleepScore
  })
  
  // 实时特征图表
  if (this.features.length > 0) {
    SleepFeatureChart({
      features: this.features.slice(-120) // 显示最近2分钟数据
    })

// 设备连接状态

  Text(${this.devices.length}个设备监测中)
    .fontSize(14)
    .fontColor('#666666')
  
  // 控制按钮
  Button(this.monitoring ? '停止监测' : '开始监测')
    .onClick(() => this.monitoring ? this.stopMonitoring() : this.startMonitoring())

}

// 其他方法…
@Component

struct SleepStageDisplay {
@Prop stage?: SleepStage;
@Prop score: number;

build() {
Column() {
if (this.stage) {
Text(this.getStageName())
.fontSize(24)
.fontColor(this.getStageColor())

    ProgressBar({
      value: this.score,
      total: 100
    })
    .width('80%')
    
    Text(睡眠质量: ${this.score.toFixed(0)}/100)
      .fontSize(18)

else {

    Text('准备开始睡眠监测')
      .fontSize(20)

}

private getStageName(): string {

switch (this.stage) {
  case 'deep': return '深睡眠';
  case 'light': return '浅睡眠';
  case 'rem': return 'REM睡眠';
  case 'awake': return '清醒';
  default: return '未知';

}

// 其他方法…
@Component

struct SleepFeatureChart {
@Prop features: SleepAudioFeature[];

build() {
// 使用XComponent绘制特征曲线
XComponent({
id: ‘feature_chart’,
type: ‘canvas’,
controller: this.chartController
})
.width(‘100%’)
.height(200)
}

三、跨设备协同关键实现
多设备数据融合

// 在SleepAudioAnalyzer中添加
async getMultiDeviceFeatures(startTime: number, endTime: number): Promise<SleepAudioFeature[]> {
const devices = await deviceManager.getTrustedDeviceListSync();
const allFeatures: SleepAudioFeature[] = [];

await Promise.all(devices.map(async device => {
try {
const remoteStore = await distributedData.getRemoteKVStore(device.deviceId, ‘sleep_features’);
const entries = await remoteStore.entries(‘feature_’);

  entries.forEach(([_, feature]) => {
    if (feature.timestamp >= startTime && feature.timestamp <= endTime) {
      allFeatures.push(feature);

});

catch (err) {

  console.error(获取设备${device.deviceId}数据失败:, err);

}));

return allFeatures.sort((a, b) => a.timestamp - b.timestamp);

分布式结果聚合

// 新增SleepReportGenerator.ts
export class SleepReportGenerator {
static async generateNightlyReport(userId: string, date: string): Promise<SleepReport> {
const features = await SleepAudioAnalyzer.getMultiDeviceFeatures(
new Date(${date}T20:00:00).getTime(),
new Date(${date}T08:00:00).getTime()
);

const stages = await SleepStageClassifier.classifyNight(features);
return {
  date,
  stages,
  score: this.calculateOverallScore(stages),
  metrics: this.calculateMetrics(features)
};

// 其他方法…

智能环境调节

// 新增EnvironmentOptimizer.ts
export class EnvironmentOptimizer {
static async adjustForSleepStage(stage: SleepStage) {
const devices = await deviceManager.getTrustedDeviceListSync();

await Promise.all(devices.map(device => {
  switch (stage) {
    case 'deep':
      return this.sendCommand(device.deviceId, {
        lights: 'dim_red',
        temperature: 20,
        sound: 'white_noise'
      });
    case 'rem':
      return this.sendCommand(device.deviceId, {
        lights: 'off',
        temperature: 22,
        sound: 'none'
      });
    // 其他阶段处理...

}));

// 其他方法…

四、性能优化方案
音频流处理优化

// 在SleepAudioAnalyzer中添加
private optimizeAudioProcessing(buffer: ArrayBuffer): ArrayBuffer {
// 降采样到8kHz单声道
const input = new Int16Array(buffer);
const output = new Int16Array(input.length / 4);

for (let i = 0; i < output.length; i++) {
output[i] = input[i * 4]; // 简单取一个通道
return output.buffer;

特征数据压缩

// 在SleepAudioAnalyzer中添加
private compressFeature(feature: SleepAudioFeature): CompressedFeature {
return {
t: feature.timestamp,
d: feature.deviceId,
s: Math.round(feature.snoreIntensity * 100),
m: Math.round(feature.movementScore * 100),
b: feature.breathingRate,
e: Math.round(feature.environmentNoise)
};

本地缓存策略

const featureCache = new Map<string, SleepAudioFeature[]>();

async getCachedFeatures(date: string): Promise<SleepAudioFeature[] | undefined> {
if (featureCache.has(date)) {
return featureCache.get(date);
const features = await this.loadNightlyFeatures(date);

if (features) {
featureCache.set(date, features);
return features;

五、应用场景扩展
鼾症预警系统

class SnoreAlert {
async checkSevereSnoring(features: SleepAudioFeature[]) {
// 检测严重打鼾模式
}

呼吸暂停监测

class ApneaDetector {
async detectApneaEvents(features: SleepAudioFeature[]) {
// 识别呼吸暂停事件
}

睡眠习惯分析

class SleepHabitAnalyzer {
async analyzeLongTermPatterns(reports: SleepReport[]) {
// 分析长期睡眠模式
}

智能闹钟系统

class SmartAlarm {
async scheduleOptimalWakeTime(sleepStages: SleepStage[]) {
// 在浅睡眠阶段唤醒用户
}

本系统充分利用HarmonyOS分布式能力，实现了：
多设备协同监测：消除监测盲区，提高数据可靠性

智能环境适应：根据睡眠阶段自动调节卧室环境

专业级分析：符合临床睡眠分期标准(R&K标准)