Overview
In response to the safety needs of delivery workers during the pandemic, we designed and developed an intelligent inflatable protective garment. This garment integrates GSR sensors, D3O high-energy absorption materials, and collision detection technology, enabling it to rapidly inflate upon impact when a delivery worker is involved in a collision, effectively providing protection and reducing accident risks. Throughout the development process, we continuously iterated and optimized through ongoing user research and field testing, ensuring that the protective garment not only meets basic safety requirements but also offers excellent wearing comfort and ease of operation.
My contribution
Led all user research and design activities.
Assisted the engineering team with hardware debugging and development.
The team
1 × product manager
1 × product designer
1 × engineers
Year
2021.09 - 2022.03
problem
Traffic accidents are prevalent in the delivery industry, putting delivery workers' lives and economic wellbeing at serious risk.
Our mixed-method research revealed that the delivery industry suffers from unclear accountability structures, creating barriers to intervention.
Strategy
We employed mixed-method research to deeply explore the real challenges faced by delivery workers and identified critical needs that had gone unexpressed. Based on feasibility and intervention assessments, we arrived at the optimal solution for the current context.
We used deep user insights to guide the design process.
From preliminary research to detailed user behavior analysis, every design decision was grounded in actual data and user feedback.
By combining qualitative and quantitative research methods, we not only identified key delivery worker needs but also uncovered inadequately addressed problems within the industry.
This ensured the solution's applicability and logic, ultimately producing a product that genuinely meets user needs while maintaining commercial viability.
Solution
We developed a "comfortable, wearable intelligent inflatable protective garment" to reduce injuries sustained by delivery workers at the moment of impact and enhance their workplace safety.
Utilizing GSR sensors and D3O materials, the garment automatically inflates upon detecting a collision to provide immediate protection. Beyond technical innovation, we conducted multiple rounds of user research and iterative feedback sessions to ensure the product is both safe and comfortable in real-world use.
项目流程
Where it all begain?
During the COVID-19 pandemic, the demand for food delivery surged, and reports about the survival struggles of delivery workers increased accordingly. As delivery workers lack a voice in society, we wanted to try to do something to help.
Initial design question
What should we create?
To address the issues or challenges that delivery workers "care about most," thereby improving their job satisfaction?
Desk Research
What we found?
| To gain a comprehensive and rapid understanding of the problem, we first conducted desk/secondary research.
Online Questionnaire Survey
Based on the three findings from the "desk research" above, we created a "questionnaire survey" to further understand the situation of delivery workers.
For this, we collected 64 valid questionnaire responses and used the Affinity mapping method for qualitative analysis to categorize the data results, with the aim of further narrowing the research scope.
Conclusion:
Delivery workers are generally dissatisfied with workplace safety levels, working hours, and the rating system.
Who Should We Interview?
Through these recruitment criteria, we aim to gain deep insights into the actual working and living conditions of delivery workers.
Our goal is to collect high-quality, representative data for subsequent analysis and strategy development, thereby helping the delivery worker community improve their career and quality of life.
🔵 "The ideal interviewee is a mainland Chinese delivery worker who is willing to maintain high work intensity and is interested in improving work and living conditions."
We established the following criteria to identify suitable participants.
Recruitment Strategy
• Conduct preliminary online surveys through delivery workers' WeChat groups to identify interested participants.
• Conduct in-depth interviews with selected candidates, either virtually or in-person.
• Ensure diversity in participant selection to cover a range of different experiences and perspectives.
Inclusion Criteria
• Active delivery workers on major food delivery service platforms in first- and second-tier cities in mainland China.。
• Open to using technology (such as apps, wearable devices, etc.) to optimize work processes.
• Willing to share experiences regarding job satisfaction and health issues, and willing to participate in discussions and evaluations of technologies or strategies that "may improve their working conditions and quality of life."
Exclusion Criteria
• People who are uncomfortable discussing their work experiences in depth or using technology.
• Delivery workers who cannot work at high intensity due to health issues or other personal reasons.
User Interviews
| 基本上一轮的调查问卷中总结出的10个发现，我们设计了一份访谈问卷（30-40分钟/人），并按照招募规则筛选出8名合格的参与者开展访谈。最终确定了4个关键发现。
本研究旨在深入理解外卖员面临的挑战，这有助于创建更契合的支持系统来改善其工作生活条件。
Persona
|
Deeper Exploration
Experiential Research
Why did I conduct "Experiential Research"?
To get closer to the essence of the problem.
To compensate for the limitation of personas in determining user need priorities.
To participate in the delivery workers' community and daily life, rather than just being an observer.
This gave me an immersive, insider perspective to understand and empathize with the real lives of delivery workers.
How did I conduct "Experiential Research"?
I registered for Meituan Crowdsourcing (part-time) and became a delivery worker.
I collected data through observation, documentation, and inquiry, and used this data to guide ideation and design.
What did I gain from "Experiential Research"?
It revealed problems that were not fully discussed in previous surveys and interviews. I distilled 3 new findings:
High-intensity work triggers health crises.
Long-term, high-intensity outdoor work exposes delivery workers to various health risks, such as fatigue, joint pain, and respiratory problems.
Platform algorithms & extreme weather jointly lead to high accident rates.
There is a high positive correlation between food delivery platform algorithms, extreme weather, and accident rates.
High-intensity work leads to cognitive decline.
After working continuously for several hours, delivery workers experience drowsiness, reduced attention, and cognitive decline.
However, under the dual constraints of economic pressure and platform rules, they have almost no choice but to continue working.
After integrating these 3 hidden issues into the previous insights, we identified the core pain points of delivery workers 👇
Problem Identification
Solution Generation
在识别，并排除了目前难以解决的结构性问题后。我们最终获得了”3“个具体的设计策略。
1. 智能防护服 —— 提供基础保护的穿戴设备
• 防护特性： 采用具有高能量吸收特性的材料制作智能防护服，如D3O。该材料在正常状态下柔软灵活，但在受到冲击时会迅速变硬，提供保护。
• 智能传感器： 集成碰撞传感器，当检测到碰撞时，智能防护服会立即激活额外的气囊保护外卖员的关键部位（如头部、胸部和脊柱）。
• 可穿戴性： 设计成轻便透气的结构，确保长时间穿戴不影响工作舒适度。
2. 智能头盔 —— 易于使用且高效的安全设备
• 疲劳监测： 内置脑波监测器和心率传感器，实时监测外卖员的注意力和疲劳状态。当注意力下降或疲劳过度时，头盔会发出警报。
• 语音助手： 集成语音助手功能，提供导航、天气预报等服务，减少外卖员在驾驶时的分心操作。
• 冷却系统： 内置微型风扇和湿度传感器，在高温天气下自动启动，提供冷却效果，提升工作舒适性。
3. 智能后台系统 —— 综合性安全保障系统
• 数据集成： 将智能防护服和智能头盔的数据集成到一个综合性后台系统，实时监控每位外卖员的工作状态。
• 紧急响应： 在检测到外卖员发生事故时，系统会立即发送紧急信号给最近的医疗服务和公司安全部门，确保及时救援。
• 健康管理： 提供个性化健康建议，定期提示检查和复健，预防职业病。
痛点及其需求是？
| 从痛点出发，我们分析并定义了外卖员的具体需求，以解决他们在工作中遇到的安全与健康问题。
随后，我们借助 KANO分析，明确了需求的优先级。
痛点
由于长时间的高强度工作和极端天气影响，外卖员面临：
注意力无法集中
长工时和恶劣天气导致注意力分散。
交通安全风险大
注意力不集中增加交通事故发生的风险。
痛点背后是
安全设备缺失
市场上缺乏能够有效提升外卖员安全的设备或系统。
经济与生命安全威胁
频繁的事故不仅威胁生命安全，还导致经济损失。
痛点背后的需求 需求排序 ( KANO分析)
| 从已识别的核心需求和研究结果出发，我们构建了下面的“问题陈述”。
受影响的人群是谁？
长时间工作的外卖员
问题何时发生？| 问题发生在哪里？
在工作高峰时段或长时间劳累之后。| 城市的繁忙交通环境中。
问题是什么？
外卖员因注意力缺失和生理疲劳导致的交通事故率远高于平均水平，可能引起职业伤害甚至生命危险。
为什么会发生这个问题？| 问题的重要性是什么？
长时间的高强度工作导致注意力和体力下降，增加了事故的风险。| 这关乎外卖员的安全与健康，同时对企业和社会造成经济及法律影响
为什么要召开一致性会议？
在明确了“用户问题”后，为了确保所有团队成员对“问题陈述的理解。
确保各方对问题的根本原因、用户需求和项目目标有一致的认知。
下一步是什么？
基于共识，我们商定出了”最终的设计问题“，指导后续的设计和研究工作。
用户面临问题是什么？
问题陈述
一致性会议
设计问题
| 哇! 我们有了一个明确清晰的设计问题。
需求分解
| 为了找到有效的解决方案，我们将复杂的DQ拆分成几个具体的小问题。
这样，我们便可以针对每个小问题进行头脑风暴，探索可行的解决方案。”
利益相关者访谈
| “围绕拆解后的三个问题，我们进行了头脑风暴，并得到了多个Design solution。
为了筛选和验证“这些Design solution”是否真正符合目标用户的核心需求，并具备良好的实施性，我们进行了利益相关者访谈。
设计方案
| “接下来，为了让该项目的时间和经费可控，我们采用How-Now-Wow 矩阵来评估这3个解决方案的“创新程度和实施难度”。
最后确定“智能防护服”为最优的解决方案。
方案评估
| “在经过一轮又一轮的用户研究和问题分解后，最终，借助 How-Now-Wow 矩阵和雷达图，我们将“智能防护服”作为该项目的解决方案。
为什么在此时进行利益相关者访谈？
正式进入开发产品前，对”设计问题“进行可行性评估，可以有效规避掉开发过程中的潜在风险。
访谈的收获?
WOW！ —— 智能防护服：具有技术创新性，实施难度可控。👍🏼
Now —— 智能头盔：现有技术支持，易于实施但缺乏技术创新。
How —— 智能后台系统：技术创新性强，但实施难度高。
技术原理
的可视化
Visualization of
technical principles
功能图示
1. 保护 Protection
这种服装配备充气气囊及安全垫以确保外卖员在配送过程中的安全。
同时通过模拟人体肌肉的工作来增强穿戴者的体力。
2. 计时器 Timing
衣服表面的”实时工作倒计时，让路人和其他车辆知道外卖员的紧急配送状态。
3. 降低风阻 Wind resistance reduction
肩部安全气囊的形状减少了风的阻力，提高了工作的效率
4. 特殊材料衣服 Fabric cloth
通过控制硅胶的流出和流入来实践人体的扩展，使穿戴者能够更好地适应不同的工作环境。
技术逻辑原理
GSR | 皮肤电反应的原理
• GSR 是由汗腺活动或交感神经系统的变化引起的皮肤电阻波动。
• 皮肤作为最敏感的生理指标，它可以准确反映人类的心理活动。
• 当身体受到感官刺激或情绪变化时，皮肤中的血管会因个体的情绪刺激而收缩和放松。
为什么可以被测量？ —— 敏感&精确
身体的汗腺分泌也会发生变化，从而导致皮肤电阻的变化形成皮肤电阻反应，且可被测量。
手掌与身体其他部位的出汗调节不同，手掌的汗腺功能主要对心理活动或感官刺激敏感。
心理活动越多或感官刺激越强烈，皮肤电响应也越明显。
如何测量“皮肤电反应”数据？
通常使用左手（非惯用手）进行数据采集，这样被试者在测试过程中可使用惯用手操作（例如点击鼠标或按下按钮以对屏幕上的刺激做出反应）。此时，可按图片所示，用魔术贴将电极置于食指和中指上。
使用场景流转图
我们的收获
What We Learned ？
让参与者推荐更多参与者
虽然验证产品的可用性至关重要，但最难且最耗时的部分是招募参与者。
我尝试过发送群发招募信息、去外卖员休息站线下招募以及利用利益相关者的推荐后，最有效的方法似乎是通过区域外卖站长推荐参与者，并鼓励参与者推荐更多人。
合作中的沟通问题
项目初期，团队成员间的沟通不够充分，导致多次返工。
随后，我们实施了每周的“一致性会议”，确保每个成员都明白项目的最新动态和即将到来的里程碑。
设计师的技术实施之旅
技术挑战和局限性
基于对项目的深入理解，构建和发布功能的方式变得更加自主。在设计智能防护服的过程中，我发现市场上常见的传感器无法达到我们对速度和准确性的预期。这促使我不断地尝试新工具和媒介，这不仅扩展了我的技能范團，还使我能够推动设计师在工程好奇心方面的可能性。
硬件的可靠性：在实地的测试中，一些原型机在恶劣天气条件下状况频发。我们回到设计板，增强了设备的防水和防尘能力，提高装置在极端天气条件下的运行稳定性。
如果项目继续，我们计划招募材料科学背景的技术支持，帮助提高设备材料的性能延长其耐用性。
用户反馈的重要性
用户测试的启示
通过实地测试，我们发现外卖员会特别在意设备的舒适性和操作简便性。
如果项目继续，我们将简化了操作流程，使其学习成本趋近于0。
同时，我们计划建立了一个反馈通道，以持续收集用户的使用感受和改进建议。为产品的未来迭代提供了方向。
Thanks for Watching