鸿蒙分布式心率监测系统：多设备协同的脉搏检测与分析方案 原创

鸿蒙分布式心率监测系统：多设备协同的脉搏检测与分析方案

一、系统架构设计

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

采用三层架构：
采集层：多设备摄像头同步采集脉搏信号

处理层：分布式信号处理与心率计算

展示层：跨终端实时可视化与异常预警

二、核心模块实现
脉搏信号处理模块

// PulseAnalyzer.ts
import image from ‘@ohos.multimedia.image’;
import signalProcessing from ‘@ohos.ai.signalProcessing’;
import distributedData from ‘@ohos.data.distributedData’;

interface PulseData {
timestamp: number;
deviceId: string;
rawSignal: number[];
heartRate?: number;
confidence: number;
export class PulseAnalyzer {

private processor: signalProcessing.SignalProcessor;
private kvManager: distributedData.KVManager;
private kvStore?: distributedData.KVStore;

async init() {
// 初始化信号处理器
this.processor = await signalProcessing.createProcessor({
algorithm: ‘ppg_peak_detection’,
sampleRate: 30 // 30fps
});

// 初始化分布式数据同步
const context = getContext(this);
this.kvManager = distributedData.createKVManager({ context });
this.kvStore = await this.kvManager.getKVStore('pulse_data', {
  createIfMissing: true,
  autoSync: true
});

async processFrame(image: image.Image): Promise<PulseData> {

// 从指尖区域提取RGB信号
const roi = this.detectFingertipROI(image);
const rgb = this.extractROIAverage(image, roi);

// 使用绿色通道作为主要PPG信号
const ppgSignal = rgb[1]; // 绿色通道

// 存储原始信号
const pulseData: PulseData = {
  timestamp: Date.now(),
  deviceId: 'local_device',
  rawSignal: [ppgSignal],
  confidence: this.calculateConfidence(ppgSignal)
};

await this.syncPulseData(pulseData);
return pulseData;

private detectFingertipROI(image: image.Image): { x: number; y: number; width: number; height: number } {

// 简化版指尖区域检测（实际项目应使用AI模型）
return {
  x: Math.floor(image.width * 0.4),
  y: Math.floor(image.height * 0.7),
  width: 100,
  height: 100
};

// 其他辅助方法…

分布式心率计算

// HeartRateCalculator.ts
import statistical from ‘@ohos.ai.statistical’;
import fft from ‘@ohos.ai.fft’;

export class HeartRateCalculator {
static async calculateFromSignals(signals: PulseData[]): Promise<number | undefined> {
if (signals.length < 30) return undefined; // 至少1秒数据(30fps)

// 合并多设备信号
const combined = this.combineSignals(signals);

// 带通滤波 (0.75Hz ~ 4Hz对应45-240bpm)
const filtered = await signalProcessing.bandpassFilter(
  combined, 
  0.75, 
  4, 
  30 // 采样率
);

// FFT频谱分析
const spectrum = fft.forward(filtered);
const peakFreq = this.findDominantFrequency(spectrum);

return Math.round(peakFreq * 60); // 转换为bpm

private static combineSignals(signals: PulseData[]): number[] {

// 按时间戳对齐并加权平均
const timeline: Record<number, { sum: number; count: number }> = {};

signals.forEach(data => {
  const timeKey = Math.floor(data.timestamp / 100); // 10ms时间窗
  if (!timeline[timeKey]) {
    timeline[timeKey] = { sum: 0, count: 0 };

timeline[timeKey].sum += data.rawSignal[0] * data.confidence;

  timeline[timeKey].count += data.confidence;
});

return Object.entries(timeline)
  .sort(([a], [b]) => parseInt(a) - parseInt(b))
  .map(([_, { sum, count }]) => sum / count);

// 其他方法…

主页面实现（ArkUI）

// HeartRateApp.ets
import { PulseAnalyzer } from ‘./PulseAnalyzer’;
import { HeartRateCalculator } from ‘./HeartRateCalculator’;

@Entry
@Component
struct HeartRateApp {
@State heartRate?: number;
@State confidence?: number;
@State deviceStatus: Record<string, boolean> = {};

private analyzer = new PulseAnalyzer();
private cameraController?: CameraController;

async aboutToAppear() {
await this.analyzer.init();
this.setupDeviceListener();
async startMeasurement() {

this.cameraController = new CameraController({
  onFrame: async (image: image.Image) => {
    const pulseData = await this.analyzer.processFrame(image);
    const signals = await this.getRecentSignals();
    
    if (signals.length >= 30) { // 有足够数据时计算心率
      this.heartRate = await HeartRateCalculator.calculateFromSignals(signals);
      this.confidence = statistical.mean(signals.map(s => s.confidence));

},

  frameRate: 30
});
this.cameraController.start();

build() {

Column() {
  // 心率显示
  if (this.heartRate !== undefined) {
    HeartRateDisplay({
      rate: this.heartRate,
      confidence: this.confidence || 0
    })

else {

    Text('请将手指覆盖摄像头')

// 设备状态

  DeviceStatus({
    devices: this.deviceStatus
  })
  
  // 控制按钮
  Button('开始测量')
    .onClick(() => this.startMeasurement())

}

// 其他方法…
@Component

struct HeartRateDisplay {
@Prop rate: number;
@Prop confidence: number;

build() {
Stack() {
// 心率数值
Text(${this.rate})
.fontSize(48)
.fontColor(this.getRateColor())

  // 置信度指示器
  Circle()
    .width(20)
    .height(20)
    .fill(this.getConfidenceColor())
    .position({ x: '80%', y: '10%' })

}

private getRateColor(): Resource {
if (this.rate < 60) return $r(‘app.color.blue’);
if (this.rate > 100) return $r(‘app.color.red’);
return $r(‘app.color.green’);
private getConfidenceColor(): Resource {

if (this.confidence > 0.8) return $r('app.color.green');
if (this.confidence > 0.5) return $r('app.color.yellow');
return $r('app.color.red');

}

@Component
struct DeviceStatus {
@Prop devices: Record<string, boolean>;

build() {
Row() {
ForEach(Object.entries(this.devices), ([id, active]) => {
Column() {
Circle()
.width(15)
.height(15)
.fill(active ? r(‘app.color.green’) : r(‘app.color.gray’))
Text(id.substring(0, 4))
.fontSize(12)
})

}

三、跨设备协同关键实现
多设备信号同步

// 在PulseAnalyzer中添加
private async syncPulseData(data: PulseData) {
if (!this.kvStore) return;

// 压缩数据
const compressed = {
t: data.timestamp,
d: data.deviceId,
s: data.rawSignal.map(v => Math.round(v * 1000) / 1000), // 保留3位小数
c: Math.round(data.confidence * 100) / 100
};

await this.kvStore.put(pulse_{data.deviceId}_{data.timestamp}, compressed);
async getRecentSignals(duration = 5000): Promise<PulseData[]> {

if (!this.kvStore) return [];

const entries = await this.kvStore.entries(‘pulse_’);
const now = Date.now();

return entries
.map(([_, v]) => v as PulseData)
.filter(d => now - d.timestamp <= duration)
.sort((a, b) => a.timestamp - b.timestamp);

设备间时钟同步

// 新增TimeSynchronizer.ts
export class TimeSynchronizer {
static async syncAcrossDevices(masterDeviceId: string) {
const devices = await deviceManager.getTrustedDeviceListSync();
const masterTime = await this.getDeviceTime(masterDeviceId);

await Promise.all(devices.map(device => 
  this.adjustDeviceClock(device.deviceId, masterTime)
));

// 其他方法…

异常心率协同检测

// 新增AnomalyDetector.ts
export class AnomalyDetector {
static async checkAbnormalHR(heartRate: number, age: number): Promise<boolean> {
const maxHR = 220 - age;
return heartRate < 50 || heartRate > maxHR * 0.9;
static async notifyEmergencyContacts(alert: HRAlert) {

const contacts = await this.getEmergencyContacts();
await Promise.all(contacts.map(contact => 
  this.sendAlert(contact, alert)
));

// 其他方法…

四、性能优化方案
信号质量评估

// 在PulseAnalyzer中添加
private calculateConfidence(signalValue: number): number {
// 简单评估信号质量（实际项目应使用更复杂算法）
const baseline = 100; // 示例基线值
const delta = Math.abs(signalValue - baseline);

if (delta < 5) return 0.9;
if (delta < 15) return 0.7;
if (delta < 30) return 0.4;
return 0.1;

自适应采样率

// 在PulseAnalyzer中添加
private adjustSamplingRate(signalQuality: number): number {
// 根据信号质量调整采样率
if (signalQuality > 0.8) return 15; // 高质量时15fps足够
if (signalQuality > 0.5) return 30; // 中等质量30fps
return 60; // 低质量时提高采样率

本地数据缓存

const signalCache = new Map<string, PulseData>();

async getCachedSignals(deviceId: string): Promise<PulseData[]> {
const cacheKey = ${deviceId}_signals;
if (signalCache.has(cacheKey)) {
return signalCache.get(cacheKey)!;
const signals = await this.getRecentSignalsFromDevice(deviceId);

signalCache.set(cacheKey, signals);
return signals;

五、应用场景扩展
健康趋势分析

class HealthTrend {
async analyzeHRV(heartRates: number[]) {
// 心率变异性分析
}

运动强度监测

class WorkoutMonitor {
async calculateExerciseIntensity(heartRate: number, age: number) {
// 计算运动强度百分比
}

压力水平评估

class StressEvaluator {
async estimateStressLevel(hrv: number) {
// 基于心率变异性评估压力
}

睡眠质量分析

class SleepAnalyzer {
async analyzeSleepPattern(heartRates: NightlyHR[]) {
// 分析睡眠阶段
}

本系统充分利用HarmonyOS分布式能力，实现了：
多设备信号融合：提高测量精度与可靠性

实时协同计算：毫秒级的心率计算结果同步

智能异常检测：基于年龄的个性化心率评估

自适应采样：根据信号质量动态调整采集策略

开发者可以基于此框架扩展更多健康监测场景：
结合血氧检测的全面健康评估

与智能手表联动的连续监测

远程医疗监护系统集成

个性化健康建议推送